1 Setup

1.1 Set chunk options & load package libraries

# set options
# This is an example setup chunk from the N741 project
knitr::opts_chunk$set(root.dir = "~/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data",
                      results = "asis",
                      echo = TRUE,
                      message = FALSE,
                      warning = FALSE,
                      tidy = TRUE,
                      tidy.opts = list(width.cutoff = 55))
# options(na.action = na.warn)??
# Load packages
library(igraph)  # package for working with and visualizing network analysis objectve
library(haven)  # package for importing SAS data files (i.e., '.sas7bdat')
library(tidyverse)  # packages for data import, cleaning, transformation, and analysis
library(gt)  # package for creating and formating latex tables
library(lubridate)  # package for working with date data
library(knitr)
# library(pander) # ????  library(printr) # ????
# library(forcats) # package for making and working
# with factors library(modelr) # package for
# statistical modeling in r
library(readxl)
library(readr)
library(stringr)
library(labelled)
library(details)
library(kableExtra)

1.2 Dataset filenames & data path

Make a path object for the data directory and print a list of all data files

data_path <- paste(getwd(), "Data", sep = "/")

# kable(tibble(list.files(data_path)), caption = 'List
# of Datasets (filenames)')
data_files <- list.files(path = data_path, pattern = "*.sas7bdat",
    full.names = TRUE)
data_files

[1] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/allshifts_edges.sas7bdat”
[2] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/allstaff_numbyshift.sas7bdat”
[3] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/censusmax_perperson.sas7bdat”
[4] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/centrality_measures.sas7bdat”
[5] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/clusterboot_patient.sas7bdat”
[6] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/clusterboot_staff.sas7bdat”
[7] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/clusterboot.sas7bdat”
[8] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/combined35a_halfyr4.sas7bdat”
[9] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/completepat.sas7bdat”
[10] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/completepat2.sas7bdat”
[11] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/completestaff.sas7bdat”
[12] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/completestaff2.sas7bdat”
[13] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/count_rooms_used.sas7bdat”
[14] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/count_rooms_used2.sas7bdat”
[15] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/countspershift.sas7bdat”
[16] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/date_shiftnum.sas7bdat”
[17] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/degree_by_edgetype.sas7bdat”
[18] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/degree_pp_resid.sas7bdat”
[19] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/degree_ps_resid.sas7bdat”
[20] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/degree_pscat_resid.sas7bdat”
[21] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/degreepscat_resids.sas7bdat”
[22] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/edges_all_allshifts.sas7bdat”
[23] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/edges_shift19.sas7bdat”
[24] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/edges2.sas7bdat”
[25] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/eventfile_long2.sas7bdat”
[26] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/eventfile_long6944_noduploc.sas7bdat”
[27] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/events_notunique_persid.sas7bdat”
[28] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/events_uniquepersid.sas7bdat”
[29] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/events_uniquepersid2.sas7bdat”
[30] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/excessiveroomchanges.sas7bdat”
[31] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/finalsids6944.sas7bdat”
[32] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/flynn_eventfile.sas7bdat”
[33] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/id_sid_matchup.sas7bdat”
[34] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/id_sid_matchup2.sas7bdat”
[35] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/location_categories_list.sas7bdat”
[36] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/location_definitions.sas7bdat”
[37] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/locationslist.sas7bdat”
[38] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/lone_all_allshifts.sas7bdat”
[39] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/network_allshifts.sas7bdat”
[40] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/network_wts_bycombo.sas7bdat”
[41] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/network_wts_overall_nolone.sas7bdat”
[42] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/network_wts_overall.sas7bdat”
[43] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/networkall_freqbyd8.sas7bdat”
[44] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/networkall_summary.sas7bdat”
[45] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/nodelevel_all_19_halfyr_patients.sas7bdat” [46] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/nodelevel_all_19_halfyr_staff.sas7bdat”
[47] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/nodelevel_all_19_halfyr.sas7bdat”
[48] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/nodup.sas7bdat”
[49] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/participationrates.sas7bdat”
[50] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/patients_in_completepat.sas7bdat”
[51] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/patients_notapproached.sas7bdat”
[52] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/patients_participating.sas7bdat”
[53] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/Patients_population.sas7bdat”
[54] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/patients_population2.sas7bdat”
[55] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/patients_populationwithsyndrome.sas7bdat” [56] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/person_dataset.sas7bdat”
[57] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/popn_patients_our_shifts7.sas7bdat”
[58] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/popn_pts_our_shifts_freqbyd8.sas7bdat”
[59] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/population_35shifts.sas7bdat”
[60] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/population_first35shifts.sas7bdat”
[61] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/staff_in_edgefile2012june.sas7bdat”
[62] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/staffsids_in_eventfile.sas7bdat”
[63] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/tommy_edges_2009.sas7bdat”
[64] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/tommy_edges_shift10_19.sas7bdat”
[65] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/tommy_nodes_2009.sas7bdat”
[66] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/tommy_nodes_shift10_19.sas7bdat”
[67] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/tommyflynn_patient_info_2009.sas7bdat”
[68] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/tommyflynn_patient_info.sas7bdat”
[69] “/Users/tommy-two/Documents/1_Research/2_Data_Science/0_Projects/1_NACI/Data/wdegree_bytype.sas7bdat”

# read all data files into a list of dfs
data_list <- map(data_files, read_sas)

map(data_list, glimpse)

Rows: 46,062 Columns: 15 $ i 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,… $ any 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,… $ staffi 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,… $ idi “7920091”, “7920091”, “7920091”, “7920091”, “7920091”, “792… $ d8 2009-07-09, 2009-07-09, 2009-07-09, 2009-07-09, 2009-07-09… $ H1N1 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,… $ quarter 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,… $ shiftampm 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,… $ d9 18087, 18087, 18087, 18087, 18087, 18087, 18087, 18087, 180… $ edgeweight 0.524722222, 3.767222222, 1.111666667, 0.487222222, 0.79361… $ j 2, 3, 4, 5, 6, 7, 8, 12, 14, 15, 19, 23, 24, 25, 26, 29, 30… $ staffj 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,… $ combo 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1,… $ idj ”7920092”, “7920093”, “7920094”, “7920095”, “7920096”, “792… $ comboc ”2 staff-staff”, “2 staff-staff”, “2 staff-staff”, “2 staff… Rows: 359 Columns: 3 $ shift_num 1, 1, 1, 1, 8, 8, 8, 8, 10, 10, 10, 10, 17, 17, 17, 17, 19,… $ JOBTITLE ”MD”, “RN”, “RN?”, “STAFF”, “MD”, “RN”, “RN?”, “STAFF”, “MD… $ staffcount 4, 7, 5, 17, 2, 11, 4, 16, 4, 8, 3, 10, 5, 11, 5, 13, 7, 13… Rows: 4,731 Columns: 3 $ SHIFT_NUM 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,… $ SID ”0002d045”, “0002d078”, “0002d098”, “0002d10b”, “0… $ censusMax_perPerson 53, 41, 53, 53, 53, 46, 41, 32, 51, 52, 41, 46, 53… Rows: 81 Columns: 3 $ shift_num 1, 8, 10, 17, 19, 23, 25, 38, 43, 47, 53, 56, 63, 67, 78, 81… $ ClosCent 9.202284, 3.470755, 4.977548, 4.693836, 26.780388, 9.014041,… $ BetCent 96076.77, 91550.93, 67926.73, 159677.63, 158402.11, 83564.92… Rows: 2,374,308 Columns: 86 $ sid ”0021159c”, “00215d4b”, “00215d82”, “00215da0… $ d8 2009-07-31, 2009-07-31, 2009-07-31, 2009-07-… $ duration_observed 252.96667, 453.00000, 96.00000, 127.90000, 77… $ censusmax_perperson 35, 38, 31, 38, 31, 38, 33, 38, 38, 38, 38, 3… $ censusmedian_perperson NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N… $ censusmean_perperson NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N… $ shift_num 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 2… $ shiftlength 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 1… $ ampm 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, … $ studyQuarter 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, … $ weekday 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, … $ staff 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … $ participant_type ”_PAT”, “_PAT”, “_PAT”, “_PAT”, “_PAT”, “_PAT… $ staffcount 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 3… $ patcount 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 7… $ census_min 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 2… $ census_max 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 3… $ patient_patient 359, 359, 359, 359, 359, 359, 359, 359, 359, … $ staff_patient 430, 430, 430, 430, 430, 430, 430, 430, 430, … $ staff_staff 400, 400, 400, 400, 400, 400, 400, 400, 400, … $ lone_patient 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … $ lone_staff 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, … $ degree 15, 21, 15, 4, 26, 21, 19, 23, 19, 17, 24, 21… $ degree_pp 11, 13, 8, 2, 10, 13, 9, 13, 10, 7, 17, 12, 6… $ degree_ps 4, 8, 7, 2, 16, 8, 10, 10, 9, 10, 7, 9, 7, 5,… $ degree_ss NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N… $ wdegree 0.96222222, 1.64722222, 0.63777778, 2.1491666… $ WDEGMIN_SS NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N… $ wdegmin_PP 56.5000000, 47.8333333, 31.4833333, 128.30000… $ wdegmin_PS_STAFF NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N… $ wdegmin_PS_pat 1.233333, 51.000000, 6.783333, 0.650000, 23.1… $ RELATIVE_DEGREE 0.13761468, 0.19266055, 0.13761468, 0.0366972… $ RELATIVE_DEGREE_PS_PAT 0.10526316, 0.21052632, 0.18421053, 0.0526315… $ RELATIVE_DEGREE_PS_STAFF NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N… $ RELATIVE_DEGREE_SS NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N… $ RELATIVE_DEGREE_PP 0.15492958, 0.18309859, 0.11267606, 0.0281690… $ LOG_WTDEGREE 0.6740776, 0.9735109, 0.4933403, 1.1471379, 0… $ Closeness_Centrality 0.5192308, 0.5373134, 0.5320197, 0.4576271, 0… $ Betweenness_Centrality 1.943202e-03, 9.593717e-03, 8.937011e-04, 1.6… $ Clustering_Coefficient 0.5047619, 0.4000000, 0.6571429, 0.8333333, 0… $ Number_of_triangles 53, 84, 69, 5, 163, 111, 86, 163, 115, 70, 13… $ Eigenvector_Centrality 0.20128667, 0.31221947, 0.29038734, 0.0812490… $ Authority 0.006433454, 0.008845999, 0.006433454, 0.0020… $ Hub 0.006433454, 0.008845999, 0.006433454, 0.0020… $ Modularity_Class 5, 4, 4, 5, 4, 5, 5, 4, 4, 4, 5, 5, 4, 4, 5, … $ PageRank 0.007127199, 0.009297011, 0.006601165, 0.0028… $ Eccentricity 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, … $ Component_ID 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … $ num_shortest_paths 11772, 11772, 11772, 11772, 11772, 11772, 117… $ weak_components 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, … $ triangle 0.5047619, 0.4000000, 0.6571429, 0.8333333, 0… $ tot_triangles 5772, 5772, 5772, 5772, 5772, 5772, 5772, 577… $ pop_high_acuity 0.2105263, 0.2105263, 0.2105263, 0.2105263, 0… $ pop_med_age 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 4… $ pop_female_freq 51.57895, 51.57895, 51.57895, 51.57895, 51.57… $ pop_black_freq 81.05263, 81.05263, 81.05263, 81.05263, 81.05… $ pop_acuity_hilo “low”, “low”, “low”, “low”, “low”, “low”, “lo… $ pop_med_age_hilo ” low”, ” low”, ” low”, ” low”, ” low”, ” low… $ pop_female_freq_hilo ” low”, ” low”, ” low”, ” low”, ” low”, ” low… $ pop_black_freq_hilo ” low”, ” low”, ” low”, ” low”, ” low”, ” low… $ timeCoveredbyShift “stay is completely in shift”, “startsDuring”… $ arrival_mode “Other”, “EMS”, “Other”, “EMS”, “Other”, “Oth… $ AGE 56, 56, 40, 88, 53, 44, 42, 23, 30, 40, 34, 5… $ Sex ”Female”, “Male”, “Male”, “Male”, “Female”, “… $ ED_Disposition ”Discharge”, “Discharge”, “Discharge”, “Admit… $ admitted ”0.00%“,”100.00%“,”0.00%“,”100.00%“,”0.00… $ acuitycat “2 Urgent”, “3 Immediate/Emergent”, “2 Urgent… $ COUNT_OF_MD 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, … $ COUNT_OF_RN 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 1… $ COUNT_OF_ST 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 1… $ Syndrome ”1 Other”, “5 ChestPain”, “1 Other”, “1 Other… $ BLACKyn ”Black”, “Black”, “Black”, “Black”, “Black”, … $ StartsDuringPct 76.38889, 76.38889, 76.38889, 76.38889, 76.38… $ musculoskel “other”, “other”, “other”, “other”, “other”, … $ pop_respir_pct 11.6, 11.6, 11.6, 11.6, 11.6, 11.6, 11.6, 11.… $ pop_musculoskel_pct 9.5, 9.5, 9.5, 9.5, 9.5, 9.5, 9.5, 9.5, 9.5, … $ pop_chestP_pct 15.8, 15.8, 15.8, 15.8, 15.8, 15.8, 15.8, 15.… $ pop_GI_pct 11.6, 11.6, 11.6, 11.6, 11.6, 11.6, 11.6, 11.… $ pop_neuro_pct 5.3, 5.3, 5.3, 5.3, 5.3, 5.3, 5.3, 5.3, 5.3, … $ pop_othersyndrome_pct 46.3, 46.3, 46.3, 46.3, 46.3, 46.3, 46.3, 46.… $ arrive_by_EMS_pct 27.7, 27.7, 27.7, 27.7, 27.7, 27.7, 27.7, 27.… $ ParticipantCat2 “PATIENT”, “PATIENT”, “PATIENT”, “PATIENT”, “… $ Num_uniqueRoomChanges 64, 20, 41, 6, 58, 165, 143, 171, 209, 64, 19… $ uniqueshiftnum 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 2… $ Replicate 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, … $ COUNT 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, … Rows: 1,263,609 Columns: 86 $ sid ”0002f35c”, “0002f445”, “0002f468”, “0002f469… $ d8 2009-07-31, 2009-07-31, 2009-07-31, 2009-07-… $ duration_observed 305.700000, 616.900000, 640.316667, 543.41666… $ censusmax_perperson 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 3… $ censusmedian_perperson 33.5, 32.0, 32.0, 32.5, 32.0, 32.0, 32.0, 32.… $ censusmean_perperson 33.55000, 32.63415, 32.52381, 32.97222, 32.76… $ shift_num 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 2… $ shiftlength 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 1… $ ampm 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, … $ studyQuarter 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, … $ weekday 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, … $ staff 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, … $ participant_type ”STAFF”, “STAFF”, “STAFF”, “STAFF”, “STAFF”, … $ staffcount 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 3… $ patcount 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 7… $ census_min 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 2… $ census_max 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 3… $ patient_patient 359, 359, 359, 359, 359, 359, 359, 359, 359, … $ staff_patient 430, 430, 430, 430, 430, 430, 430, 430, 430, … $ staff_staff 400, 400, 400, 400, 400, 400, 400, 400, 400, … $ lone_patient 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … $ lone_staff 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, … $ degree 25, 35, 31, 33, 31, 33, 30, 49, 26, 60, 26, 1… $ degree_pp NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N… $ degree_ps 1, 6, 3, 4, 3, 4, 3, 23, 1, 30, 5, 4, 2, 17, … $ degree_ss 24, 29, 28, 29, 28, 29, 27, 26, 25, 30, 21, 1… $ wdegree 42.47055556, 65.79111111, 72.42444444, 71.732… $ WDEGMIN_SS 2548.083333, 3942.400000, 4329.183333, 4300.3… $ wdegmin_PP NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N… $ wdegmin_PS_STAFF 0.1500000, 5.0666667, 16.2833333, 3.6333333, … $ wdegmin_PS_pat NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N… $ RELATIVE_DEGREE 0.22935780, 0.32110092, 0.28440367, 0.3027522… $ RELATIVE_DEGREE_PS_PAT NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N… $ RELATIVE_DEGREE_PS_STAFF 0.01388889, 0.08333333, 0.04166667, 0.0555555… $ RELATIVE_DEGREE_SS 0.64864865, 0.78378378, 0.75675676, 0.7837837… $ RELATIVE_DEGREE_PP NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N… $ LOG_WTDEGREE 3.7720838, 4.2015700, 4.2962569, 4.2867883, 4… $ Closeness_Centrality 0.5373134, 0.5869565, 0.5775401, 0.5775401, 0… $ Betweenness_Centrality 0.0003972054, 0.0043499584, 0.0017483375, 0.0… $ Clustering_Coefficient 0.9066667, 0.6840336, 0.7935484, 0.7178030, 0… $ Number_of_triangles 272, 407, 369, 379, 359, 389, 351, 511, 293, … $ Eigenvector_Centrality 0.57107307, 0.72857176, 0.68580348, 0.6992781… $ Authority 0.010454362, 0.014475271, 0.012866908, 0.0136… $ Hub 0.010454362, 0.014475271, 0.012866908, 0.0136… $ Modularity_Class 2, 0, 2, 2, 0, 0, 2, 2, 2, 4, 1, 1, 2, 4, 0, … $ PageRank 0.009317580, 0.012781202, 0.011361925, 0.0120… $ Eccentricity 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, … $ Component_ID 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … $ num_shortest_paths 11772, 11772, 11772, 11772, 11772, 11772, 117… $ weak_components 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, … $ triangle 0.9066667, 0.6840336, 0.7935484, 0.7178030, 0… $ tot_triangles 5772, 5772, 5772, 5772, 5772, 5772, 5772, 577… $ pop_high_acuity 0.2105263, 0.2105263, 0.2105263, 0.2105263, 0… $ pop_med_age 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 4… $ pop_female_freq 51.57895, 51.57895, 51.57895, 51.57895, 51.57… $ pop_black_freq 81.05263, 81.05263, 81.05263, 81.05263, 81.05… $ pop_acuity_hilo “low”, “low”, “low”, “low”, “low”, “low”, “lo… $ pop_med_age_hilo ” low”, ” low”, ” low”, ” low”, ” low”, ” low… $ pop_female_freq_hilo ” low”, ” low”, ” low”, ” low”, ” low”, ” low… $ pop_black_freq_hilo ” low”, ” low”, ” low”, ” low”, ” low”, ” low… $ timeCoveredbyShift ““,”“,”“,”“,”“,”“,”“,”“,”“,”“,”“,”… $ arrival_mode “EMS”, “Other”, ““,”“,”“,”“,”“,”“,”“,”… $ AGE NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N… $ Sex ““,”“,”“,”“,”“,”“,”“,”“,”“,”“,”“,”… $ ED_Disposition ““,”“,”“,”“,”“,”“,”“,”“,”“,”“,”“,”… $ admitted ““,”“,”“,”“,”“,”“,”“,”“,”“,”“,”“,”… $ acuitycat ““,”“,”“,”“,”“,”“,”“,”“,”“,”“,”“,”… $ COUNT_OF_MD 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, … $ COUNT_OF_RN 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 1… $ COUNT_OF_ST 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 1… $ Syndrome ““,”“,”“,”“,”“,”“,”“,”“,”“,”“,”“,”… $ BLACKyn ““,”“,”“,”“,”“,”“,”“,”“,”“,”“,”“,”… $ StartsDuringPct 76.38889, 76.38889, 76.38889, 76.38889, 76.38… $ musculoskel ““,”“,”“,”“,”“,”“,”“,”“,”“,”“,”“,”… $ pop_respir_pct 11.6, 11.6, 11.6, 11.6, 11.6, 11.6, 11.6, 11.… $ pop_musculoskel_pct 9.5, 9.5, 9.5, 9.5, 9.5, 9.5, 9.5, 9.5, 9.5, … $ pop_chestP_pct 15.8, 15.8, 15.8, 15.8, 15.8, 15.8, 15.8, 15.… $ pop_GI_pct 11.6, 11.6, 11.6, 11.6, 11.6, 11.6, 11.6, 11.… $ pop_neuro_pct 5.3, 5.3, 5.3, 5.3, 5.3, 5.3, 5.3, 5.3, 5.3, … $ pop_othersyndrome_pct 46.3, 46.3, 46.3, 46.3, 46.3, 46.3, 46.3, 46.… $ arrive_by_EMS_pct 27.7, 27.7, 27.7, 27.7, 27.7, 27.7, 27.7, 27.… $ ParticipantCat2 “STAFF”, “STAFF”, “STAFF”, “STAFF”, “STAFF”, … $ Num_uniqueRoomChanges 3, 43, 40, 27, 42, 22, 4, 30, 26, 310, 11, 6,… $ uniqueshiftnum 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 2… $ Replicate 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, … $ COUNT 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, … Rows: 3,637,917 Columns: 86 $ sid “0021159c”, “00215d4b”, “00215d82”, “00215da0… $ d8 2009-07-31, 2009-07-31, 2009-07-31, 2009-07-… $ duration_observed 252.96667, 453.00000, 96.00000, 127.90000, 77… $ censusmax_perperson 35, 38, 31, 38, 31, 38, 33, 38, 38, 38, 38, 3… $ censusmedian_perperson NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N… $ censusmean_perperson NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N… $ shift_num 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 2… $ shiftlength 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 1… $ ampm 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, … $ studyQuarter 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, … $ weekday 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, … $ staff 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … $ participant_type ”_PAT”, “_PAT”, “_PAT”, “_PAT”, “_PAT”, “_PAT… $ staffcount 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 3… $ patcount 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 7… $ census_min 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 2… $ census_max 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 3… $ patient_patient 359, 359, 359, 359, 359, 359, 359, 359, 359, … $ staff_patient 430, 430, 430, 430, 430, 430, 430, 430, 430, … $ staff_staff 400, 400, 400, 400, 400, 400, 400, 400, 400, … $ lone_patient 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … $ lone_staff 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, … $ degree 15, 21, 15, 4, 26, 21, 19, 23, 19, 17, 24, 21… $ degree_pp 11, 13, 8, 2, 10, 13, 9, 13, 10, 7, 17, 12, 6… $ degree_ps 4, 8, 7, 2, 16, 8, 10, 10, 9, 10, 7, 9, 7, 5,… $ degree_ss NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N… $ wdegree 0.96222222, 1.64722222, 0.63777778, 2.1491666… $ WDEGMIN_SS NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N… $ wdegmin_PP 56.5000000, 47.8333333, 31.4833333, 128.30000… $ wdegmin_PS_STAFF NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N… $ wdegmin_PS_pat 1.233333, 51.000000, 6.783333, 0.650000, 23.1… $ RELATIVE_DEGREE 0.13761468, 0.19266055, 0.13761468, 0.0366972… $ RELATIVE_DEGREE_PS_PAT 0.10526316, 0.21052632, 0.18421053, 0.0526315… $ RELATIVE_DEGREE_PS_STAFF NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N… $ RELATIVE_DEGREE_SS NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N… $ RELATIVE_DEGREE_PP 0.15492958, 0.18309859, 0.11267606, 0.0281690… $ LOG_WTDEGREE 0.6740776, 0.9735109, 0.4933403, 1.1471379, 0… $ Closeness_Centrality 0.5192308, 0.5373134, 0.5320197, 0.4576271, 0… $ Betweenness_Centrality 1.943202e-03, 9.593717e-03, 8.937011e-04, 1.6… $ Clustering_Coefficient 0.5047619, 0.4000000, 0.6571429, 0.8333333, 0… $ Number_of_triangles 53, 84, 69, 5, 163, 111, 86, 163, 115, 70, 13… $ Eigenvector_Centrality 0.20128667, 0.31221947, 0.29038734, 0.0812490… $ Authority 0.006433454, 0.008845999, 0.006433454, 0.0020… $ Hub 0.006433454, 0.008845999, 0.006433454, 0.0020… $ Modularity_Class 5, 4, 4, 5, 4, 5, 5, 4, 4, 4, 5, 5, 4, 4, 5, … $ PageRank 0.007127199, 0.009297011, 0.006601165, 0.0028… $ Eccentricity 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, … $ Component_ID 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … $ num_shortest_paths 11772, 11772, 11772, 11772, 11772, 11772, 117… $ weak_components 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, … $ triangle 0.5047619, 0.4000000, 0.6571429, 0.8333333, 0… $ tot_triangles 5772, 5772, 5772, 5772, 5772, 5772, 5772, 577… $ pop_high_acuity 0.2105263, 0.2105263, 0.2105263, 0.2105263, 0… $ pop_med_age 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 4… $ pop_female_freq 51.57895, 51.57895, 51.57895, 51.57895, 51.57… $ pop_black_freq 81.05263, 81.05263, 81.05263, 81.05263, 81.05… $ pop_acuity_hilo “low”, “low”, “low”, “low”, “low”, “low”, “lo… $ pop_med_age_hilo ” low”, ” low”, ” low”, ” low”, ” low”, ” low… $ pop_female_freq_hilo ” low”, ” low”, ” low”, ” low”, ” low”, ” low… $ pop_black_freq_hilo ” low”, ” low”, ” low”, ” low”, ” low”, ” low… $ timeCoveredbyShift “stay is completely in shift”, “startsDuring”… $ arrival_mode “Other”, “EMS”, “Other”, “EMS”, “Other”, “Oth… $ AGE 56, 56, 40, 88, 53, 44, 42, 23, 30, 40, 34, 5… $ Sex ”Female”, “Male”, “Male”, “Male”, “Female”, “… $ ED_Disposition ”Discharge”, “Discharge”, “Discharge”, “Admit… $ admitted ”0.00%“,”100.00%“,”0.00%“,”100.00%“,”0.00… $ acuitycat “2 Urgent”, “3 Immediate/Emergent”, “2 Urgent… $ COUNT_OF_MD 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, … $ COUNT_OF_RN 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 1… $ COUNT_OF_ST 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 1… $ Syndrome ”1 Other”, “5 ChestPain”, “1 Other”, “1 Other… $ BLACKyn ”Black”, “Black”, “Black”, “Black”, “Black”, … $ StartsDuringPct 76.38889, 76.38889, 76.38889, 76.38889, 76.38… $ musculoskel “other”, “other”, “other”, “other”, “other”, … $ pop_respir_pct 11.6, 11.6, 11.6, 11.6, 11.6, 11.6, 11.6, 11.… $ pop_musculoskel_pct 9.5, 9.5, 9.5, 9.5, 9.5, 9.5, 9.5, 9.5, 9.5, … $ pop_chestP_pct 15.8, 15.8, 15.8, 15.8, 15.8, 15.8, 15.8, 15.… $ pop_GI_pct 11.6, 11.6, 11.6, 11.6, 11.6, 11.6, 11.6, 11.… $ pop_neuro_pct 5.3, 5.3, 5.3, 5.3, 5.3, 5.3, 5.3, 5.3, 5.3, … $ pop_othersyndrome_pct 46.3, 46.3, 46.3, 46.3, 46.3, 46.3, 46.3, 46.… $ arrive_by_EMS_pct 27.7, 27.7, 27.7, 27.7, 27.7, 27.7, 27.7, 27.… $ ParticipantCat2 “PATIENT”, “PATIENT”, “PATIENT”, “PATIENT”, “… $ Num_uniqueRoomChanges 64, 20, 41, 6, 58, 165, 143, 171, 209, 64, 19… $ uniqueshiftnum 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 2… $ Replicate 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, … $ COUNT 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, … Rows: 35 Columns: 111 $ shift_num 1, 8, 10, 17, 19, 23, 25, 38, 43, 47, 5… $ shift ”1pm”, “8pm”, “10am”, “17pm”, “19pm”, “… $ shift_num_ampm ”1pm”, “8pm”, “10am”, “17pm”, “19pm”, “… $ month 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 9, 9, … $ day_of_month 9, 16, 18, 25, 27, 31, 2, 15, 20, 24, 3… $ d8 2009-07-09, 2009-07-16, 2009-07-18, 20… $ H1N1 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, … $ weekday 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, … $ day_of_week 5, 5, 7, 7, 2, 6, 1, 7, 5, 2, 1, 4, 4, … $ staffcount 33, 33, 25, 34, 44, 38, 26, 35, 33, 33,… $ patcount 74, 82, 64, 95, 89, 72, 61, 91, 80, 76,… $ total 107, 115, 89, 129, 133, 110, 87, 126, 1… $ COUNT_OF_MD 4, 2, 4, 5, 7, 5, 2, 9, 2, 5, 5, 8, 5, … $ COUNT_OF_RN 12, 15, 11, 16, 18, 16, 12, 15, 16, 14,… $ COUNT_OF_ST 17, 16, 10, 13, 19, 17, 12, 11, 15, 14,… $ possiblecombs 5671, 6555, 3916, 8256, 8778, 5995, 374… $ total_patients 98, 117, 82, 108, 117, 95, 77, 133, 109… $ participationrate 75.51020, 70.08547, 78.04878, 87.96296,… $ patient_patient 379, 535, 219, 591, 502, 359, 174, 472,… $ staff_patient 411, 490, 180, 478, 389, 430, 243, 548,… $ staff_staff 263, 276, 147, 277, 415, 400, 184, 270,… $ lone_patient 0, 1, 1, 2, 0, 0, 2, 1, 0, 0, 0, 2, 1, … $ lone_staff 1, 2, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, … $ total_edges 1053, 1301, 546, 1346, 1306, 1189, 601,… $ Num_Pat_MD 55, 10, 30, 78, 56, 52, 36, 47, 16, 26,… $ Num_Pat_RN 55, 69, 43, 80, 41, 66, 44, 73, 63, 49,… $ Num_Pat_ST 50, 40, 28, 42, 72, 60, 23, 47, 64, 37,… $ Num_Pat_Pat 74, 80, 63, 91, 86, 72, 58, 90, 78, 76,… $ pat_pct_PP 100.00000, 97.56098, 98.43750, 95.78947… $ pat_pct_PMD 74.324324, 12.195122, 46.875000, 82.105… $ pat_pct_PRN 74.32432, 84.14634, 67.18750, 84.21053,… $ pat_pct_PST 67.567568, 48.780488, 43.750000, 44.210… $ day_of_week1 5, 5, 7, 7, 2, 6, 1, 7, 5, 2, 1, 4, 4, … $ weekday_0_1 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, … $ shift_ampm ”pm”, “pm”, “am”, “pm”, “pm”, “am”, “am… $ shift_d8_ampm ”200979pm”, “2009716pm”, “2009718am”, “… $ studyQuarter 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, … $ firsthalf_day_of_week 5, 5, NA, 7, 2, NA, NA, NA, 5, NA, 1, N… $ indexc 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, … $ NUMSHIFT 1, 8, 10, 17, 19, 23, 25, 38, 43, 47, 5… $ day_of_week2 6, 6, NA, 1, 3, NA, NA, NA, 6, NA, 2, N… $ day1 ”5 Thu”, “5 Thu”, “7 Sat”, “7 Sat”, “2 … $ day2 ”Fri”, “Fri”, ““,”Sun”, “Tue”, ““,”“,… $ dayofweek ”5 Thu Fri”, “5 Thu Fri”, “7 Sat”, “7 S… $ quarter_year 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, … $ ShiftStart

[[2]] # A tibble: 359 × 3 shift_num JOBTITLE staffcount 1 1 MD 4 2 1 RN 7 3 1 RN? 5 4 1 STAFF 17 5 8 MD 2 6 8 RN 11 7 8 RN? 4 8 8 STAFF 16 9 10 MD 4 10 10 RN 8 # … with 349 more rows

[[3]] # A tibble: 4,731 × 3 SHIFT_NUM SID censusMax_perPerson 1 1 0002d045 53 2 1 0002d078 41 3 1 0002d098 53 4 1 0002d10b 53 5 1 0002d151 53 6 1 0002d7c3 46 7 1 0002e016 41 8 1 0002e0d1 32 9 1 0002e120 51 10 1 0002e34f 52 # … with 4,721 more rows

[[4]] # A tibble: 81 × 3 shift_num ClosCent BetCent 1 1 9.20 96077. 2 8 3.47 91551. 3 10 4.98 67927. 4 17 4.69 159678. 5 19 26.8 158402. 6 23 9.01 83565. 7 25 3.22 59941. 8 38 11.4 122549. 9 43 11.5 100956. 10 47 14.7 62668. # … with 71 more rows

[[5]] # A tibble: 2,374,308 × 86 sid d8 duration_observed censusmax_perperson censusmedian_perpe… 1 0021159c 2009-07-31 253. 35 NA 2 00215d4b 2009-07-31 453. 38 NA 3 00215d82 2009-07-31 96.0 31 NA 4 00215da0 2009-07-31 128. 38 NA 5 00215dbb 2009-07-31 77.0 31 NA 6 00215e12 2009-07-31 182. 38 NA 7 00215e17 2009-07-31 128. 33 NA 8 00215e43 2009-07-31 282. 38 NA 9 00215e68 2009-07-31 241. 38 NA 10 00215e69 2009-07-31 250. 38 NA # … with 2,374,298 more rows, and 81 more variables: # censusmean_perperson , shift_num , shiftlength , ampm , # studyQuarter , weekday , staff , participant_type , # staffcount , patcount , census_min , census_max , # patient_patient , staff_patient , staff_staff , # lone_patient , lone_staff , degree , degree_pp , # degree_ps , degree_ss , wdegree , WDEGMIN_SS , …

[[6]] # A tibble: 1,263,609 × 86 sid d8 duration_observed censusmax_perperson censusmedian_perpe… 1 0002f35c 2009-07-31 306. 38 33.5 2 0002f445 2009-07-31 617. 38 32
3 0002f468 2009-07-31 640. 38 32
4 0002f469 2009-07-31 543. 38 32.5 5 0002f46c 2009-07-31 590. 38 32
6 0002f472 2009-07-31 673. 38 32
7 0002f495 2009-07-31 565. 38 32
8 0002f49c 2009-07-31 706. 38 32
9 0002f4a3 2009-07-31 392. 38 34
10 0002f4e2 2009-07-31 317. 38 34
# … with 1,263,599 more rows, and 81 more variables: # censusmean_perperson , shift_num , shiftlength , ampm , # studyQuarter , weekday , staff , participant_type , # staffcount , patcount , census_min , census_max , # patient_patient , staff_patient , staff_staff , # lone_patient , lone_staff , degree , degree_pp , # degree_ps , degree_ss , wdegree , WDEGMIN_SS , …

[[7]] # A tibble: 3,637,917 × 86 sid d8 duration_observed censusmax_perperson censusmedian_perpe… 1 0021159c 2009-07-31 253. 35 NA 2 00215d4b 2009-07-31 453. 38 NA 3 00215d82 2009-07-31 96.0 31 NA 4 00215da0 2009-07-31 128. 38 NA 5 00215dbb 2009-07-31 77.0 31 NA 6 00215e12 2009-07-31 182. 38 NA 7 00215e17 2009-07-31 128. 33 NA 8 00215e43 2009-07-31 282. 38 NA 9 00215e68 2009-07-31 241. 38 NA 10 00215e69 2009-07-31 250. 38 NA # … with 3,637,907 more rows, and 81 more variables: # censusmean_perperson , shift_num , shiftlength , ampm , # studyQuarter , weekday , staff , participant_type , # staffcount , patcount , census_min , census_max , # patient_patient , staff_patient , staff_staff , # lone_patient , lone_staff , degree , degree_pp , # degree_ps , degree_ss , wdegree , WDEGMIN_SS , …

[[8]] # A tibble: 35 × 111 shift_num shift shift_num_ampm month day_of_month d8 H1N1 weekday 1 1 1pm 1pm 7 9 2009-07-09 0 1 2 8 8pm 8pm 7 16 2009-07-16 0 1 3 10 10am 10am 7 18 2009-07-18 0 0 4 17 17pm 17pm 7 25 2009-07-25 0 0 5 19 19pm 19pm 7 27 2009-07-27 0 1 6 23 23am 23am 7 31 2009-07-31 0 1 7 25 25am 25am 8 2 2009-08-02 0 0 8 38 38am 38am 8 15 2009-08-15 1 0 9 43 43pm 43pm 8 20 2009-08-20 1 1 10 47 47am 47am 8 24 2009-08-24 1 1 # … with 25 more rows, and 103 more variables: day_of_week , # staffcount , patcount , total , COUNT_OF_MD , # COUNT_OF_RN , COUNT_OF_ST , possiblecombs , # total_patients , participationrate , patient_patient , # staff_patient , staff_staff , lone_patient , # lone_staff , total_edges , Num_Pat_MD , Num_Pat_RN , # Num_Pat_ST , Num_Pat_Pat , pat_pct_PP , pat_pct_PMD , …

[[9]] # A tibble: 4,732 × 43,209 sid shift_num_ampm d8 day mon year floc1 floc2 floc3 floc4 floc5 1 0002d045 ” 1… 18087 9 7 2009 NA NA NA NA NA 2 0002d098 ” 1… 18087 9 7 2009 NA NA NA NA NA 3 0002d10b ” 1… 18087 9 7 2009 NA NA NA NA NA 4 0002e120 ” 1… 18087 9 7 2009 NA NA NA NA NA 5 0002e875 ” 1… 18087 9 7 2009 NA NA NA NA NA 6 0002e9c5 ” 1… 18087 9 7 2009 NA NA NA NA NA 7 0002ea0a ” 1… 18087 9 7 2009 NA NA NA NA NA 8 0002f43e ” 1… 18087 9 7 2009 NA NA NA NA NA 9 0002f43f ” 1… 18087 9 7 2009 NA NA NA NA NA 10 0002f443 ” 1… 18087 9 7 2009 NA NA NA NA NA # … with 4,722 more rows, and 43,198 more variables: floc6 , floc7 , # floc8 , floc9 , floc10 , floc11 , floc12 , # floc13 , floc14 , floc15 , floc16 , floc17 , # floc18 , floc19 , floc20 , floc21 , floc22 , # floc23 , floc24 , floc25 , floc26 , floc27 , # floc28 , floc29 , floc30 , floc31 , floc32 , # floc33 , floc34 , floc35 , floc36 , floc37 , …

[[10]] # A tibble: 4,732 × 43,219 sid shift_num_ampm d8 day mon year floc1 floc2 floc3 floc4 1 0002d045 ” 1p… 2009-07-09 9 7 2009 NA NA NA NA 2 0002d078 ” 1p… 2009-07-10 9 7 2009 NA NA NA NA 3 0002d098 ” 1p… 2009-07-09 9 7 2009 NA NA NA NA 4 0002d10b ” 1p… 2009-07-09 9 7 2009 NA NA NA NA 5 0002d151 ” 1p… 2009-07-10 9 7 2009 NA NA NA NA 6 0002d7c3 ” 1p… 2009-07-10 9 7 2009 NA NA NA NA 7 0002e016 ” 1p… 2009-07-10 9 7 2009 NA NA NA NA 8 0002e0d1 ” 1p… 2009-07-10 9 7 2009 NA NA NA NA 9 0002e120 ” 1p… 2009-07-09 9 7 2009 NA NA NA NA 10 0002e34f ” 1p… 2009-07-10 9 7 2009 NA NA NA NA # … with 4,722 more rows, and 43,209 more variables: floc5 , floc6 , # floc7 , floc8 , floc9 , floc10 , floc11 , # floc12 , floc13 , floc14 , floc15 , floc16 , # floc17 , floc18 , floc19 , floc20 , floc21 , # floc22 , floc23 , floc24 , floc25 , floc26 , # floc27 , floc28 , floc29 , floc30 , floc31 , # floc32 , floc33 , floc34 , floc35 , floc36 , …

[[11]] # A tibble: 2,218 × 43,209 sid d8 day year shift_num_ampm mon floc1 floc2 floc3 floc4 1 0002f4e2 2009-07-09 9 2009 ” 1p… 7 NA NA NA NA 2 00101f96 2009-07-09 9 2009 ” 1p… 7 NA NA NA NA 3 00102288 2009-07-09 9 2009 ” 1p… 7 NA NA NA NA 4 0002f35c 2009-07-10 9 2009 ” 1p… 7 NA NA NA NA 5 0002f445 2009-07-10 9 2009 ” 1p… 7 NA NA NA NA 6 0002f468 2009-07-10 9 2009 ” 1p… 7 NA NA NA NA 7 0002f469 2009-07-10 9 2009 ” 1p… 7 NA NA NA NA 8 0002f46c 2009-07-10 9 2009 ” 1p… 7 NA NA NA NA 9 0002f472 2009-07-10 9 2009 ” 1p… 7 NA NA NA NA 10 0002f495 2009-07-10 9 2009 ” 1p… 7 NA NA NA NA # … with 2,208 more rows, and 43,199 more variables: floc5 , floc6 , # floc7 , floc8 , floc9 , floc10 , floc11 , # floc12 , floc13 , floc14 , floc15 , floc16 , # floc17 , floc18 , floc19 , floc20 , floc21 , # floc22 , floc23 , floc24 , floc25 , floc26 , # floc27 , floc28 , floc29 , floc30 , floc31 , # floc32 , floc33 , floc34 , floc35 , floc36 , …

[[12]] # A tibble: 2,218 × 43,219 sid d8 day year shift_num_ampm mon floc1 floc2 floc3 floc4 1 0002f35c 2009-07-10 9 2009 ” 1p… 7 NA NA NA NA 2 0002f35c 2009-07-17 16 2009 ” 8p… 7 NA NA NA NA 3 0002f35c 2009-07-26 25 2009 ” 17p… 7 NA NA NA NA 4 0002f35c 2009-07-28 27 2009 ” 19p… 7 NA NA NA NA 5 0002f35c 2009-07-31 31 2009 ” 23a… 7 NA NA NA NA 6 0002f35c 2009-08-02 2 2009 ” 25a… 8 NA NA NA NA 7 0002f35c 2009-08-15 15 2009 ” 38a… 8 NA NA NA NA 8 0002f35c 2009-08-21 20 2009 ” 43p… 8 NA NA NA NA 9 0002f35c 2009-08-24 24 2009 ” 47a… 8 NA NA NA NA 10 0002f35c 2009-08-30 30 2009 ” 53p… 8 NA NA NA NA # … with 2,208 more rows, and 43,209 more variables: floc5 , floc6 , # floc7 , floc8 , floc9 , floc10 , floc11 , # floc12 , floc13 , floc14 , floc15 , floc16 , # floc17 , floc18 , floc19 , floc20 , floc21 , # floc22 , floc23 , floc24 , floc25 , floc26 , # floc27 , floc28 , floc29 , floc30 , floc31 , # floc32 , floc33 , floc34 , floc35 , floc36 , …

[[13]] # A tibble: 188 × 5 ParticipantCat2 room locindex COUNT PERCENT 1 PATIENT ED RADIOLOGY 1 1037 0.909 2 STAFF ED RADIOLOGY 1 236 0.207 3 PATIENT ED ROOM 1 2 1320 1.16
4 STAFF ED ROOM 1 2 128 0.112 5 PATIENT ED ROOM 2 3 1333 1.17
6 STAFF ED ROOM 2 3 125 0.110 7 PATIENT ED ROOM 3 4 1270 1.11
8 STAFF ED ROOM 3 4 109 0.0956 9 PATIENT ED ROOM 4 5 1411 1.24
10 STAFF ED ROOM 4 5 119 0.104 # … with 178 more rows

[[14]] # A tibble: 188 × 5 room Area locindex ParticipantCat2 COUNT 1 ED RADIOLOGY Diagnotics 1 PATIENT 1037 2 ED RADIOLOGY Diagnotics 1 STAFF 236 3 ED ROOM 1 Patient Care - Acute Care 2 PATIENT 1320 4 ED ROOM 1 Patient Care - Acute Care 2 STAFF 128 5 ED ROOM 2 Patient Care - Acute Care 3 PATIENT 1333 6 ED ROOM 2 Patient Care - Acute Care 3 STAFF 125 7 ED ROOM 3 Patient Care - Acute Care 4 PATIENT 1270 8 ED ROOM 3 Patient Care - Acute Care 4 STAFF 109 9 ED ROOM 4 Patient Care - Acute Care 5 PATIENT 1411 10 ED ROOM 4 Patient Care - Acute Care 5 STAFF 119 # … with 178 more rows

[[15]] # A tibble: 81 × 9 shift_num_ampm staffcount patcount mon day total possiblecombs d8

1 ” 1… 33 74 7 9 107 5671 2009-07-09 2 ” 8… 33 82 7 16 115 6555 2009-07-16 3 ” 10… 25 64 7 18 89 3916 2009-07-18 4 ” 17… 34 95 7 25 129 8256 2009-07-25 5 ” 19… 44 89 7 27 133 8778 2009-07-27 6 ” 23… 38 72 7 31 110 5995 2009-07-31 7 ” 25… 26 61 8 2 87 3741 2009-08-02 8 ” 38… 35 91 8 15 126 7875 2009-08-15 9 ” 43… 33 80 8 20 113 6328 2009-08-20 10 ” 47… 33 76 8 24 109 5886 2009-08-24 # … with 71 more rows, and 1 more variable: shift_num

[[16]] # A tibble: 81 × 2 shift_num date

1 1 2009-07-09 2 8 2009-07-16 3 10 2009-07-18 4 17 2009-07-25 5 19 2009-07-27 6 23 2009-07-31 7 25 2009-08-02 8 38 2009-08-15 9 43 2009-08-20 10 47 2009-08-24 # … with 71 more rows

[[17]] # A tibble: 16,989 × 11 D8 i participant_type combo4 degree_bytype wtdegree_bytype 1 2009-07-09 1 STAFF MD-STAFF 1 0.471 2 2009-07-09 1 STAFF RN-STAFF 6 3.78
3 2009-07-09 1 STAFF STAFF-PAT 13 3.40
4 2009-07-09 1 STAFF STAFF-STAFF 11 13.8
5 2009-07-09 2 STAFF MD-STAFF 1 0.0186 6 2009-07-09 2 STAFF RN-STAFF 7 28.0
7 2009-07-09 2 STAFF STAFF-PAT 5 0.104 8 2009-07-09 2 STAFF STAFF-STAFF 13 44.3
9 2009-07-09 3 STAFF MD-STAFF 1 0.265 10 2009-07-09 3 STAFF RN-STAFF 8 20.2
# … with 16,979 more rows, and 5 more variables: edgeweight_Min , # edgeweight_Max , edgeweight_Mean , edgeweight_StdDev , # edgeweight_Median

[[18]] # A tibble: 4,731 × 155 shift_num sid ID i D8 startd8time ShiftStart

[[19]] # A tibble: 4,731 × 155 shift_num sid ID i D8 startd8time ShiftStart

[[20]] # A tibble: 14,193 × 156 shift_num sid ID i D8 startd8time ShiftStart

[[21]] # A tibble: 14,193 × 156 shift_num sid ID i D8 startd8time ShiftStart

[[22]] # A tibble: 45,877 × 14 i j edgeweight any staffi staffj combo idi idj d8 H1N1 1 1 12 0.562 1 1 1 2 7920… 7920… 2009-07-09 0 2 1 14 5.04 1 1 1 2 7920… 7920… 2009-07-09 0 3 1 15 0.0961 1 1 1 2 7920… 7920… 2009-07-09 0 4 1 19 0.719 1 1 1 2 7920… 7920… 2009-07-09 0 5 1 2 0.525 1 1 1 2 7920… 7920… 2009-07-09 0 6 1 23 0.471 1 1 1 2 7920… 7920… 2009-07-09 0 7 1 24 0.2 1 1 1 2 7920… 7920… 2009-07-09 0 8 1 25 0.719 1 1 1 2 7920… 7920… 2009-07-09 0 9 1 26 0.701 1 1 1 2 7920… 7920… 2009-07-09 0 10 1 29 0.0825 1 1 1 2 7920… 7920… 2009-07-09 0 # … with 45,867 more rows, and 3 more variables: quarter , # shiftampm , d9

[[23]] # A tibble: 1,306 × 29 numshift shiftampm D8 d9 H1N1 quarter sidi sidj i j 1 19 2 2009-07-27 18105 0 1 0002f35c 0002f… 1 3 2 19 2 2009-07-27 18105 0 1 0002f35c 0002f… 1 4 3 19 2 2009-07-27 18105 0 1 0002f35c 0002f… 1 5 4 19 2 2009-07-27 18105 0 1 0002f35c 0002f… 1 6 5 19 2 2009-07-27 18105 0 1 0002f35c 0002f… 1 7 6 19 2 2009-07-27 18105 0 1 0002f35c 0002f… 1 8 7 19 2 2009-07-27 18105 0 1 0002f35c 0002f… 1 9 8 19 2 2009-07-27 18105 0 1 0002f35c 0002f… 1 10 9 19 2 2009-07-27 18105 0 1 0002f35c 0002f… 1 11 10 19 2 2009-07-27 18105 0 1 0002f35c 0002f… 1 14 # … with 1,296 more rows, and 19 more variables: idi , idj , # i_participant_type , j_participant_type , staffi , # staffj , anycontact , combo , comboc , combo4 , # MD_CONTACTS , RN_CONTACTS , STAFF_CONTACTS , # PAT_CONTACTS , MD_WITHWHOM , RN_WITHWHOM , # STAFF_WITHWHOM , PAT_WITHWHOM , edgeweight

[[24]] # A tibble: 46,062 × 29 numshift shiftampm D8 d9 H1N1 quarter sidi sidj i j 1 1 2 2009-07-09 18087 0 1 0002f35c 0002f… 1 2 2 1 2 2009-07-09 18087 0 1 0002f35c 0002f… 1 3 3 1 2 2009-07-09 18087 0 1 0002f35c 0002f… 1 4 4 1 2 2009-07-09 18087 0 1 0002f35c 0002f… 1 5 5 1 2 2009-07-09 18087 0 1 0002f35c 0002f… 1 6 6 1 2 2009-07-09 18087 0 1 0002f35c 0002f… 1 7 7 1 2 2009-07-09 18087 0 1 0002f35c 0002f… 1 8 8 1 2 2009-07-09 18087 0 1 0002f35c 0002f… 1 12 9 1 2 2009-07-09 18087 0 1 0002f35c 0002f… 1 14 10 1 2 2009-07-09 18087 0 1 0002f35c 0002f… 1 15 # … with 46,052 more rows, and 19 more variables: idi , idj , # i_participant_type , j_participant_type , staffi , # staffj , anycontact , combo , comboc , combo4 , # MD_CONTACTS , RN_CONTACTS , STAFF_CONTACTS , # PAT_CONTACTS , MD_WITHWHOM , RN_WITHWHOM , # STAFF_WITHWHOM , PAT_WITHWHOM , edgeweight

[[25]] # A tibble: 382,904 × 66 sid room et event session lpindex locindex eventtime d8

1 0002d045 EXPRESS C… 2009… 1 0 707 32 1.56e9 2009-07-09 2 0002d045 ED ROOM 13 2009… 3 0 707 14 1.56e9 2009-07-09 3 0002d045 EXPRESS C… 2009… 3 0 707 33 1.56e9 2009-07-09 4 0002d045 EXPRESS C… 2009… 3 0 707 32 1.56e9 2009-07-09 5 0002d045 HALL H8 -… 2009… 3 0 707 30 1.56e9 2009-07-09 6 0002d045 EXPRESS C… 2009… 3 0 707 32 1.56e9 2009-07-09 7 0002d045 EXPRESS C… 2009… 1 0 707 33 1.56e9 2009-07-09 8 0002d045 EXPRESS C… 2009… 3 0 707 32 1.56e9 2009-07-09 9 0002d045 EXPRESS C… 2009… 3 0 707 41 1.56e9 2009-07-09 10 0002d045 EXPRESS C… 2009… 3 0 707 43 1.56e9 2009-07-09 # … with 382,894 more rows, and 57 more variables: ti

[[26]] # A tibble: 114,072 × 67 shift_num sid room et event session lpindex locindex eventtime 1 1 0002d045 AMBULANCE … 2009… 3 0 707 55 1.56e9 2 1 0002d045 CDU N.S. H… 2009… 3 0 707 57 1.56e9 3 1 0002d045 CDU NURSE … 2009… 3 0 707 61 1.56e9 4 1 0002d045 DECONTAMIN… 2009… 3 0 707 22 1.56e9 5 1 0002d045 ED NURSE S… 2009… 3 0 707 29 1.56e9 6 1 0002d045 ED ROOM 13 2009… 3 0 707 14 1.56e9 7 1 0002d045 ED ROOM 15 2009… 3 0 707 17 1.56e9 8 1 0002d045 EXPRESS CA… 2009… 3 0 707 43 1.56e9 9 1 0002d045 EXPRESS CA… 2009… 3 0 707 41 1.56e9 10 1 0002d045 EXPRESS CA… 2009… 1 0 707 32 1.56e9 # … with 114,062 more rows, and 58 more variables: d8 , ti

[[27]] # A tibble: 6,944 × 5 shift_num sid ParticipantCat2 COUNT PERCENT 1 1 0002d045 PATIENT 22 0.0193 2 1 0002d078 PATIENT 16 0.0140 3 1 0002d098 PATIENT 5 0.00438 4 1 0002d10b PATIENT 21 0.0184 5 1 0002d151 PATIENT 8 0.00701 6 1 0002d7c3 PATIENT 17 0.0149 7 1 0002e016 PATIENT 29 0.0254 8 1 0002e0d1 PATIENT 34 0.0298 9 1 0002e120 PATIENT 26 0.0228 10 1 0002e34f PATIENT 12 0.0105 # … with 6,934 more rows

[[28]] # A tibble: 6,944 × 5 shift_num sid ParticipantCat2 COUNT PERCENT 1 1 0002d045 PATIENT 29 0.00768 2 1 0002d078 PATIENT 101 0.0267 3 1 0002d098 PATIENT 6 0.00159 4 1 0002d10b PATIENT 32 0.00847 5 1 0002d151 PATIENT 78 0.0207 6 1 0002d7c3 PATIENT 74 0.0196 7 1 0002e016 PATIENT 147 0.0389 8 1 0002e0d1 PATIENT 79 0.0209 9 1 0002e120 PATIENT 53 0.0140 10 1 0002e34f PATIENT 59 0.0156 # … with 6,934 more rows

[[29]] # A tibble: 6,944 × 4 shift_num sid ParticipantCat2 Num_uniqueRoomChanges 1 1 0002d045 PATIENT 29 2 1 0002d078 PATIENT 101 3 1 0002d098 PATIENT 6 4 1 0002d10b PATIENT 32 5 1 0002d151 PATIENT 78 6 1 0002d7c3 PATIENT 74 7 1 0002e016 PATIENT 147 8 1 0002e0d1 PATIENT 79 9 1 0002e120 PATIENT 53 10 1 0002e34f PATIENT 59 # … with 6,934 more rows

[[30]] # A tibble: 338 × 3 shift_num sid num_roomchanges 1 1 0002f4e2 196 2 1 0003030e 252 3 1 00101f9f 152 4 1 00101fa7 430 5 8 0002f4e2 288 6 8 00101f7e 210 7 8 00101fa7 451 8 8 0021822a 214 9 8 00218252 172 10 8 0021833b 164 # … with 328 more rows

[[31]] # A tibble: 6,944 × 2 shift_num sid

1 1 0002d045 2 1 0002d078 3 1 0002d098 4 1 0002d10b 5 1 0002d151 6 1 0002d7c3 7 1 0002e016 8 1 0002e0d1 9 1 0002e120 10 1 0002e34f # … with 6,934 more rows

[[32]] # A tibble: 56,272 × 30 shift_num shift_num_ampm shift_d8_ampm sid ParticipantCat4 ParticipantCat2
1 1 1pm 200979pm 0002d… PATIENT PATIENT
2 1 1pm 200979pm 0002d… PATIENT PATIENT
3 1 1pm 200979pm 0002d… PATIENT PATIENT
4 1 1pm 200979pm 0002d… PATIENT PATIENT
5 1 1pm 200979pm 0002d… PATIENT PATIENT
6 1 1pm 200979pm 0002d… PATIENT PATIENT
7 1 1pm 200979pm 0002d… PATIENT PATIENT
8 1 1pm 200979pm 0002d… PATIENT PATIENT
9 1 1pm 200979pm 0002d… PATIENT PATIENT
10 1 1pm 200979pm 0002d… PATIENT PATIENT
# … with 56,262 more rows, and 24 more variables: room , et , # d8 , ti

[[33]] # A tibble: 6,950 × 5 sid day mon staff newsid 1 0002f35c 9 7 1 1 2 0002f445 9 7 1 2 3 0002f468 9 7 1 3 4 0002f469 9 7 1 4 5 0002f46c 9 7 1 5 6 0002f472 9 7 1 6 7 0002f495 9 7 1 7 8 0002f4a3 9 7 1 8 9 0002f4e2 9 7 1 9 10 0002f4e8 9 7 1 10 # … with 6,940 more rows

[[34]] # A tibble: 6,950 × 19 sid day mon staff newsid year d8 ShiftStart ShiftEnd

[[35]] # A tibble: 15 × 2 Location_Category Location_Cat_Area_sqft 1 Administrative Support 3036 2 Clinical Support 1089 3 Contingency 215 4 Diagnotics 327 5 Patient Care - Acute Care 3123 6 Patient Care - CDU 1013 7 Patient Care - Express Care 1202 8 Patient Care - Hall 1339. 9 Patient Care - Treatment 269 10 Patient Care - Triage & Registration 777 11 Primary Waiting Area 3766 12 Restroom 580 13 Secondary Waiting Area 567 14 Staff Support 4852 15 Transit 3353.

[[36]] # A tibble: 94 × 7 CorrectRoomNum locindex Location_Name Location_Area_SqFt comment
1 67 68 IMAGING AND CONF RM 314 “”
2 70 71 ED CONF. ROOM 381 “”
3 74 75 OFFICE AREA 938 “”
4 76 77 OFFICE AREA 708 “”
5 77 78 STAFF BREAK AREA 695 “”
6 24 25 CLEAN UTILITY 143 “”
7 25 26 SOILED UTILITY 157 “”
8 59 60 CDU UTILITY-STORAGE 245 “”
9 66 67 ED STORAGE 391 “”
10 78 79 EMERG LAB AREA 99 “”
# … with 84 more rows, and 2 more variables: Location_Category , # Location_CategoryN

[[37]] # A tibble: 94 × 6 Location_ID Location_Name Location_Area_SqFt comment Location_Category
1 68 IMAGING AND CONF RM 314 “” Administrative Su… 2 71 ED CONF. ROOM 381 “” Administrative Su… 3 75 OFFICE AREA 938 “” Administrative Su… 4 77 OFFICE AREA 708 “” Administrative Su… 5 78 STAFF BREAK AREA 695 “” Administrative Su… 6 25 CLEAN UTILITY 143 “” Clinical Support
7 26 SOILED UTILITY 157 “” Clinical Support
8 60 CDU UTILITY-STORAGE 245 “” Clinical Support
9 67 ED STORAGE 391 “” Clinical Support
10 79 EMERG LAB AREA 99 “” Clinical Support
# … with 84 more rows, and 1 more variable: Location_CategoryN

[[38]] # A tibble: 185 × 11 i any staffi idi d8 H1N1 quarter shiftampm d9 weekday 1 18 0 1 79200918 2009-07-09 0 1 2 18087 0 2 25 0 1 716200925 2009-07-16 0 1 2 18094 0 3 31 0 1 716200931 2009-07-16 0 1 2 18094 0 4 43 0 0 716200943 2009-07-16 0 1 2 18094 0 5 18 0 1 718200918 2009-07-18 0 1 1 18096 0 6 38 0 0 718200938 2009-07-18 0 1 1 18096 0 7 113 0 0 72520091… 2009-07-25 0 1 2 18103 0 8 122 0 0 72520091… 2009-07-25 0 1 2 18103 0 9 25 0 1 725200925 2009-07-25 0 1 2 18103 0 10 24 0 1 731200924 2009-07-31 0 1 1 18109 0 # … with 175 more rows, and 1 more variable: weight

[[39]] # A tibble: 46,062 × 15 i any staffi idi d8 H1N1 quarter shiftampm d9 edgeweight 1 34 1 0 79200… 2009-07-09 0 1 2 18087 0.0639 2 34 1 0 79200… 2009-07-09 0 1 2 18087 0.0806 3 34 1 0 79200… 2009-07-09 0 1 2 18087 0.221
4 34 1 0 79200… 2009-07-09 0 1 2 18087 0.0706 5 34 1 0 79200… 2009-07-09 0 1 2 18087 0.0119 6 34 1 0 79200… 2009-07-09 0 1 2 18087 0.00306 7 34 1 0 79200… 2009-07-09 0 1 2 18087 0.109
8 34 1 0 79200… 2009-07-09 0 1 2 18087 0.00333 9 35 1 0 79200… 2009-07-09 0 1 2 18087 0.124
10 35 1 0 79200… 2009-07-09 0 1 2 18087 0.126
# … with 46,052 more rows, and 5 more variables: j , staffj , # combo , idj , comboc

[[40]] # A tibble: 342 × 12 d8 comboc _TYPE_ _FREQ_ n sum mean median min q1 1 2009-07-09 0 pati… 0 379 379 67.2 0.177 0.0442 2.78e-4 0.00667 2 2009-07-09 1 staf… 0 411 411 90.4 0.220 0.0281 2.78e-4 0.00472 3 2009-07-09 2 staf… 0 263 263 537. 2.04 0.548 2.78e-4 0.0331 4 2009-07-09 lone s… 0 1 1 0 0 0 0 0
5 2009-07-16 0 pati… 0 535 535 111. 0.208 0.0553 2.78e-4 0.00778 6 2009-07-16 1 staf… 0 490 490 85.9 0.175 0.0958 2.78e-4 0.0131 7 2009-07-16 2 staf… 0 276 276 869. 3.15 0.752 2.78e-4 0.0221 8 2009-07-16 lone p… 0 1 1 0 0 0 0 0
9 2009-07-16 lone s… 0 2 2 0 0 0 0 0
10 2009-07-18 0 pati… 0 219 219 34.1 0.156 0.0244 2.78e-4 0.00694 # … with 332 more rows, and 2 more variables: q3 , max

[[41]] # A tibble: 81 × 11 d8 _TYPE_ _FREQ_ n sum mean median min q1 q3 1 2009-07-09 0 1053 1053 695. 0.660 0.0575 0.000278 0.00722 0.340 2 2009-07-16 0 1301 1301 1066. 0.819 0.0878 0.000278 0.0111 0.248 3 2009-07-18 0 546 546 253. 0.463 0.0512 0.000278 0.00917 0.341 4 2009-07-25 0 1346 1346 731. 0.543 0.0642 0.000278 0.00861 0.228 5 2009-07-27 0 1306 1306 906. 0.693 0.0876 0.000278 0.0131 0.432 6 2009-07-31 0 1189 1189 667. 0.561 0.0514 0.000278 0.00833 0.294 7 2009-08-02 0 601 601 290. 0.482 0.0578 0.000278 0.0075 0.269 8 2009-08-15 0 1290 1290 751. 0.582 0.0828 0.000278 0.0172 0.291 9 2009-08-20 0 997 997 674. 0.676 0.0519 0.000278 0.00889 0.328 10 2009-08-24 0 1015 1015 432. 0.426 0.0944 0.000278 0.0172 0.402 # … with 71 more rows, and 1 more variable: max

[[42]] # A tibble: 81 × 11 d8 _TYPE_ _FREQ_ n sum mean median min q1 q3 1 2009-07-09 0 1054 1054 695. 0.659 0.0569 0 0.00694 0.340 2 2009-07-16 0 1304 1304 1066. 0.817 0.0875 0 0.0108 0.248 3 2009-07-18 0 548 548 253. 0.461 0.0511 0 0.00875 0.335 4 2009-07-25 0 1349 1349 731. 0.542 0.0639 0 0.00861 0.226 5 2009-07-27 0 1306 1306 906. 0.693 0.0876 0.000278 0.0131 0.432 6 2009-07-31 0 1190 1190 667. 0.560 0.0514 0 0.00833 0.294 7 2009-08-02 0 604 604 290. 0.480 0.0575 0 0.0075 0.264 8 2009-08-15 0 1291 1291 751. 0.582 0.0825 0 0.0172 0.291 9 2009-08-20 0 998 998 674. 0.676 0.0519 0 0.00889 0.328 10 2009-08-24 0 1015 1015 432. 0.426 0.0944 0.000278 0.0172 0.402 # … with 71 more rows, and 1 more variable: max

[[43]] # A tibble: 81 × 3 d8 tot_EdgeAndLone PERCENT 1 2009-07-09 1054 2.29 2 2009-07-16 1304 2.83 3 2009-07-18 548 1.19 4 2009-07-25 1349 2.93 5 2009-07-27 1306 2.84 6 2009-07-31 1190 2.58 7 2009-08-02 604 1.31 8 2009-08-15 1291 2.80 9 2009-08-20 998 2.17 10 2009-08-24 1015 2.20 # … with 71 more rows

[[44]] # A tibble: 81 × 11 shift_num shift_num_ampm d8 numnodes staffcount patcount tot_edges 1 1 ” 1pm” 2009-07-09 107 33 74 1053 2 8 ” 8pm” 2009-07-16 115 33 82 1301 3 10 ” 10am” 2009-07-18 89 25 64 546 4 17 ” 17pm” 2009-07-25 129 34 95 1346 5 19 ” 19pm” 2009-07-27 133 44 89 1306 6 23 ” 23am” 2009-07-31 110 38 72 1189 7 25 ” 25am” 2009-08-02 87 26 61 601 8 38 ” 38am” 2009-08-15 126 35 91 1290 9 43 ” 43pm” 2009-08-20 113 33 80 997 10 47 ” 47am” 2009-08-24 109 33 76 1015 # … with 71 more rows, and 4 more variables: tot_lone , # tot_edgeAndlone , possiblecombs , pctOfPossibleContacts

[[45]] # A tibble: 2,374 × 203 sid d8 day year startd8time endd8time
1 0002d045 2009-07-09 NA NA 2009-07-09 20:00:00 NA
2 0002d078 2009-07-09 NA NA 2009-07-09 20:00:00 NA
3 0002d098 2009-07-09 NA NA 2009-07-09 20:00:00 NA
4 0002d10b 2009-07-09 NA NA 2009-07-09 20:00:00 NA
5 0002d151 2009-07-09 NA NA 2009-07-09 20:00:00 NA
6 0002d7c3 2009-07-09 NA NA 2009-07-09 20:00:00 NA
7 0002e016 2009-07-09 NA NA 2009-07-09 20:00:00 NA
8 0002e0d1 2009-07-09 NA NA 2009-07-09 20:00:00 NA
9 0002e120 2009-07-09 NA NA 2009-07-09 20:00:00 NA
10 0002e34f 2009-07-09 NA NA 2009-07-09 20:00:00 NA
# … with 2,364 more rows, and 197 more variables: shift_num_ampm , # mon , starttime , endtime , sdatetime , # edatetime , duration_observed , censusmax_perperson , # censusmedian_perperson , censusmean_perperson , shift_num , # ID , i , ShiftStart

[[46]] # A tibble: 1,263 × 203 sid d8 day year startd8time endd8time

1 0002f35c 2009-07-10 9 2009 2009-07-09 20:00:00 2009-07-10 07:00:00 2 0002f445 2009-07-10 9 2009 2009-07-09 20:00:00 2009-07-10 07:00:00 3 0002f468 2009-07-10 9 2009 2009-07-09 20:00:00 2009-07-10 07:00:00 4 0002f469 2009-07-10 9 2009 2009-07-09 20:00:00 2009-07-10 07:00:00 5 0002f46c 2009-07-10 9 2009 2009-07-09 20:00:00 2009-07-10 07:00:00 6 0002f472 2009-07-10 9 2009 2009-07-09 20:00:00 2009-07-10 07:00:00 7 0002f495 2009-07-10 9 2009 2009-07-09 20:00:00 2009-07-10 07:00:00 8 0002f4a3 2009-07-10 9 2009 2009-07-09 20:00:00 2009-07-10 07:00:00 9 0002f4e2 2009-07-09 9 2009 2009-07-09 20:00:00 2009-07-10 07:00:00 10 0002f4e8 2009-07-10 9 2009 2009-07-09 20:00:00 2009-07-10 07:00:00 # … with 1,253 more rows, and 197 more variables: shift_num_ampm , # mon , starttime , endtime , sdatetime , # edatetime , duration_observed , censusmax_perperson , # censusmedian_perperson , censusmean_perperson , shift_num , # ID , i , ShiftStart

[[47]] # A tibble: 3,637 × 203 sid d8 day year startd8time endd8time
1 0002d045 2009-07-09 NA NA 2009-07-09 20:00:00 NA
2 0002d078 2009-07-09 NA NA 2009-07-09 20:00:00 NA
3 0002d098 2009-07-09 NA NA 2009-07-09 20:00:00 NA
4 0002d10b 2009-07-09 NA NA 2009-07-09 20:00:00 NA
5 0002d151 2009-07-09 NA NA 2009-07-09 20:00:00 NA
6 0002d7c3 2009-07-09 NA NA 2009-07-09 20:00:00 NA
7 0002e016 2009-07-09 NA NA 2009-07-09 20:00:00 NA
8 0002e0d1 2009-07-09 NA NA 2009-07-09 20:00:00 NA
9 0002e120 2009-07-09 NA NA 2009-07-09 20:00:00 NA
10 0002e34f 2009-07-09 NA NA 2009-07-09 20:00:00 NA
# … with 3,627 more rows, and 197 more variables: shift_num_ampm , # mon , starttime , endtime , sdatetime , # edatetime , duration_observed , censusmax_perperson , # censusmedian_perperson , censusmean_perperson , shift_num , # ID , i , ShiftStart

[[48]] # A tibble: 3,837,853 × 15 sid room et event session lpindex locindex eventtime d8

1 0002d036 OFFICE AR… 2009… 19 0 829 77 1.57e9 2009-08-17 2 0002d036 OFFICE AR… 2009… 8 0 829 77 1.57e9 2009-08-17 3 0002d03e OFFICE AR… 2009… 1 0 716 77 1.56e9 2009-06-07 4 0002d03e OFFICE AR… 2009… 1 0 716 77 1.56e9 2009-06-08 5 0002d03e OFFICE AR… 2009… 1 0 716 77 1.56e9 2009-06-09 6 0002d03e OFFICE AR… 2009… 1 0 716 77 1.56e9 2009-06-10 7 0002d03e OFFICE AR… 2009… 1 0 716 77 1.56e9 2009-06-11 8 0002d03e OFFICE AR… 2009… 1 0 716 77 1.56e9 2009-06-12 9 0002d03e OFFICE AR… 2009… 1 0 716 77 1.56e9 2009-06-13 10 0002d03e OFFICE AR… 2009… 1 0 716 77 1.56e9 2009-06-13 # … with 3,837,843 more rows, and 6 more variables: ti

[[49]] # A tibble: 81 × 4 numshift Network_N participant_N particPCT 1 1 98 75 76.5 2 8 117 82 70.1 3 10 82 64 78.0 4 17 108 95 88.0 5 19 117 92 78.6 6 23 95 72 75.8 7 25 77 61 79.2 8 38 133 96 72.2 9 43 109 80 73.4 10 47 112 77 68.8 # … with 71 more rows

[[50]] # A tibble: 4,732 × 2 shift_num_ampm sid

1 ” 1pm” 0002d045 2 ” 1pm” 0002d078 3 ” 1pm” 0002d098 4 ” 1pm” 0002d10b 5 ” 1pm” 0002d151 6 ” 1pm” 0002d7c3 7 ” 1pm” 0002e016 8 ” 1pm” 0002e0d1 9 ” 1pm” 0002e120 10 ” 1pm” 0002e34f # … with 4,722 more rows

[[51]] # A tibble: 3,121 × 77 numshift dateTimeStart dateTimeEnd sid d8 shift_ampm
1 1 2009-07-09 20:00:00 2009-07-10 07:00:00 “” 2009-07-09 “”
2 1 2009-07-09 20:00:00 2009-07-10 07:00:00 “” 2009-07-09 “”
3 1 2009-07-09 20:00:00 2009-07-10 07:00:00 “” 2009-07-09 “”
4 1 2009-07-09 20:00:00 2009-07-10 07:00:00 “” 2009-07-09 “”
5 1 2009-07-09 20:00:00 2009-07-10 07:00:00 “” 2009-07-09 “”
6 1 2009-07-09 20:00:00 2009-07-10 07:00:00 “” 2009-07-10 “”
7 1 2009-07-09 20:00:00 2009-07-10 07:00:00 “” 2009-07-10 “”
8 1 2009-07-09 20:00:00 2009-07-10 07:00:00 “” 2009-07-10 “”
9 8 2009-07-16 19:00:00 2009-07-17 07:00:00 “” 2009-07-16 “”
10 8 2009-07-16 19:00:00 2009-07-17 07:00:00 “” 2009-07-16 “”
# … with 3,111 more rows, and 71 more variables: Reason_shortShift , # startd8time , endd8time , shift_d8_ampm , quarter , # weekday , H1N1 , SevenToTwelve , dup_tag , # dup_samePT , keepDrop , time_difference , random , # EncounterNum , ED_ARRIVAL , ed_departure , patient , # tag_starttime , tagofftime , CHANGESID , ad8 , # dd8 , a , b , sid_ , tag_problem , …

[[52]] # A tibble: 4,732 × 76 shift_num person sid mon staff d8 shift_ampm Reason_shortShift
1 1 34 0002d045 7 0 2009-07-09 pm “”
2 1 35 0002d078 7 0 2009-07-09 pm “”
3 1 36 0002d098 7 0 2009-07-09 pm “”
4 1 37 0002d10b 7 0 2009-07-09 pm “”
5 1 38 0002d151 7 0 2009-07-09 pm “”
6 1 39 0002d7c3 7 0 2009-07-09 pm “”
7 1 40 0002e016 7 0 2009-07-09 pm “”
8 1 41 0002e0d1 7 0 2009-07-09 pm “”
9 1 42 0002e120 7 0 2009-07-09 pm “”
10 1 43 0002e34f 7 0 2009-07-09 pm “”
# … with 4,722 more rows, and 68 more variables: startd8time , # shift_d8_ampm , shift_num_ampm , quarter , weekday , # H1N1 , SevenToTwelve , numshift , endd8time , # dup_tag , dup_samePT , keepDrop , time_difference , # random , EncounterNum , ED_ARRIVAL , ed_departure , # patient , tag_starttime , tagofftime , CHANGESID , # ad8 , dd8 , a , b , sid_ , tag_problem , …

[[53]] # A tibble: 9,183 × 75 sid numshift d8 shift_ampm Reason_shortShi… startd8time

1 “” 1 2009-07-09 “” “” NA
2 “” 1 2009-07-09 “” “” NA
3 “” 1 2009-07-09 “” “” NA
4 “” 1 2009-07-09 “” “” NA
5 “” 1 2009-07-09 “” “” NA
6 “” 1 2009-07-10 “” “” NA
7 “” 1 2009-07-10 “” “” NA
8 “” 1 2009-07-10 “” “” NA
9 “0002f63… 1 2009-07-09”pm” “” 2009-07-09 20:00:00 10 “0003031… 1 2009-07-10”pm” “” 2009-07-10 00:00:00 # … with 9,173 more rows, and 69 more variables: endd8time , # shift_d8_ampm , quarter , weekday , H1N1 , # SevenToTwelve , dup_tag , dup_samePT , keepDrop , # time_difference , random , EncounterNum , ED_ARRIVAL , # ed_departure , patient , tag_starttime , # tagofftime , CHANGESID , ad8 , dd8 , a , # b , sid_ , tag_problem , durationInED , …

[[54]] # A tibble: 3,117 × 83 numshift dateTimeStart dateTimeEnd sid d8 shift_ampm
1 1 2009-07-09 20:00:00 2009-07-10 07:00:00 “” 2009-07-09 “”
2 1 2009-07-09 20:00:00 2009-07-10 07:00:00 “” 2009-07-09 “”
3 1 2009-07-09 20:00:00 2009-07-10 07:00:00 “” 2009-07-09 “”
4 1 2009-07-09 20:00:00 2009-07-10 07:00:00 “” 2009-07-09 “”
5 1 2009-07-09 20:00:00 2009-07-10 07:00:00 “” 2009-07-09 “”
6 1 2009-07-09 20:00:00 2009-07-10 07:00:00 “” 2009-07-10 “”
7 1 2009-07-09 20:00:00 2009-07-10 07:00:00 “” 2009-07-10 “”
8 1 2009-07-09 20:00:00 2009-07-10 07:00:00 “” 2009-07-10 “”
9 8 2009-07-16 19:00:00 2009-07-17 07:00:00 “” 2009-07-16 “”
10 8 2009-07-16 19:00:00 2009-07-17 07:00:00 “” 2009-07-16 “”
# … with 3,107 more rows, and 77 more variables: Reason_shortShift , # startd8time , endd8time , shift_d8_ampm , quarter , # weekday , H1N1 , SevenToTwelve , dup_tag , # dup_samePT , keepDrop , time_difference , random , # EncounterNum , ED_ARRIVAL , ed_departure , patient , # tag_starttime , tagofftime , CHANGESID , ad8 , # dd8 , a , b , sid_ , tag_problem , …

[[55]] # A tibble: 9,183 × 96 sid shift_num d8 shift_ampm Reason_shortShi… startd8time

1 “” 1 2009-07-09 “” “” NA
2 “” 1 2009-07-09 “” “” NA
3 “” 1 2009-07-09 “” “” NA
4 “” 1 2009-07-09 “” “” NA
5 “” 1 2009-07-09 “” “” NA
6 “” 1 2009-07-10 “” “” NA
7 “” 1 2009-07-10 “” “” NA
8 “” 1 2009-07-10 “” “” NA
9 “0002f6… 1 2009-07-09”pm” “” 2009-07-09 20:00:00 10 “000303… 1 2009-07-10”pm” “” 2009-07-10 00:00:00 # … with 9,173 more rows, and 90 more variables: endd8time , # shift_d8_ampm , quarter , weekday , H1N1 , # SevenToTwelve , dup_tag , dup_samePT , keepDrop , # time_difference , random , EncounterNum , ED_ARRIVAL , # ed_departure , patient , tag_starttime , # tagofftime , CHANGESID , ad8 , dd8 , a , # b , sid_ , tag_problem , durationInED , …

[[56]] # A tibble: 6,944 × 5 shiftnum person staff degree minutes 1 1 1 1 31 1288. 2 1 2 1 26 4346. 3 1 3 1 34 3466. 4 1 4 1 33 920. 5 1 5 1 34 4467. 6 1 6 1 20 3651. 7 1 7 1 27 3613. 8 1 8 1 25 3913. 9 1 9 1 44 318. 10 1 10 1 5 61.7 # … with 6,934 more rows

[[57]] # A tibble: 9,183 × 75 sid numshift d8 shift_ampm Reason_shortShi… startd8time

1 “” 1 2009-07-09 “” “” NA
2 “” 1 2009-07-09 “” “” NA
3 “” 1 2009-07-09 “” “” NA
4 “” 1 2009-07-09 “” “” NA
5 “” 1 2009-07-09 “” “” NA
6 “” 1 2009-07-10 “” “” NA
7 “” 1 2009-07-10 “” “” NA
8 “” 1 2009-07-10 “” “” NA
9 “0002f63… 1 2009-07-09”pm” “” 2009-07-09 20:00:00 10 “0003031… 1 2009-07-10”pm” “” 2009-07-10 00:00:00 # … with 9,173 more rows, and 69 more variables: endd8time , # shift_d8_ampm , quarter , weekday , H1N1 , # SevenToTwelve , dup_tag , dup_samePT , keepDrop , # time_difference , random , EncounterNum , ED_ARRIVAL , # ed_departure , patient , tag_starttime , # tagofftime , CHANGESID , ad8 , dd8 , a , # b , sid_ , tag_problem , durationInED , …

[[58]] # A tibble: 81 × 3 SHIFT_NUM_AMPM COUNT PERCENT 1 102pm 108 1.22 2 104am 76 0.860 3 10am 75 0.848 4 112pm 107 1.21 5 114am 109 1.23 6 132am 105 1.19 7 138am 111 1.26 8 145am 122 1.38 9 148pm 106 1.20 10 155pm 94 1.06 # … with 71 more rows

[[59]] # A tibble: 3,853 × 75 sid numshift d8 shift_ampm Reason_shortShi… startd8time

1 “” 1 2009-07-09 “” “” NA
2 “” 1 2009-07-09 “” “” NA
3 “” 1 2009-07-09 “” “” NA
4 “” 1 2009-07-09 “” “” NA
5 “” 1 2009-07-09 “” “” NA
6 “” 1 2009-07-10 “” “” NA
7 “” 1 2009-07-10 “” “” NA
8 “” 1 2009-07-10 “” “” NA
9 “0002f63… 1 2009-07-09”pm” “” 2009-07-09 20:00:00 10 “0003031… 1 2009-07-10”pm” “” 2009-07-10 00:00:00 # … with 3,843 more rows, and 69 more variables: endd8time , # shift_d8_ampm , quarter , weekday , H1N1 , # SevenToTwelve , dup_tag , dup_samePT , keepDrop , # time_difference , random , EncounterNum , ED_ARRIVAL , # ed_departure , patient , tag_starttime , # tagofftime , CHANGESID , ad8 , dd8 , a , # b , sid_ , tag_problem , durationInED , …

[[60]] # A tibble: 28,717 × 24 ID ED_Arrival_Timestamp Patient Sex Patient_Age_at_Visit Race

1 39228 2009-07-01 16:31:00 6667316 Female 26 Not Recorded 2 14491 2009-07-01 04:46:00 32277 Male 37 Black
3 37311 2009-07-01 02:12:00 6570260 Male 38 White
4 8277 2009-07-01 17:06:00 324587 Female 45 Black
5 8335 2009-07-01 05:48:00 2133619 Male 49 Black
6 39233 2009-07-01 18:24:00 6667370 Male 17 Black
7 39202 2009-07-01 10:17:00 6666623 Female 0 White
8 37174 2009-07-01 04:50:00 6562334 Male 59 Black
9 39194 2009-07-01 08:51:00 1582437 Male 37 Black
10 39221 2009-07-01 15:33:59 6667213 Male 41 Black
# … with 28,707 more rows, and 18 more variables: Chief_Complaint , # Acuity , Arr_Mode , ED_Departure_Timestamp , # ED_Disposition , admitted , d8 , d8ARR , # d8DEP , timeARR

[[61]] # A tibble: 88 × 6 sid num_shifts_withthisSID job job_SAH ParticipantCat4 JOBTITLE
1 0002f449 1 “” RN RN RN
2 0010224d 1 “” RN RN RN
3 0010228b 1 “” RN RN RN
4 0002f447 2 “” RN RN RN
5 0002f501 2 “” STAFF STAFF STAFF
6 00101a41 2 “MD” MD MD MD
7 00101dc7 2 “” RN RN RN
8 00101f21 2 “MD” MD MD MD
9 00101f70 2 “RN” RN RN RN
10 00101f97 2 “NT” RN RN RN?
# … with 78 more rows

[[62]] # A tibble: 95 × 5 sid job job_SAH ParticipantCat4 JOBTITLE
1 0002f35c “” STAFF STAFF STAFF
2 0002f43c “” RN RN RN
3 0002f445 “” STAFF STAFF STAFF
4 0002f447 “” RN RN RN
5 0002f449 “” RN RN RN
6 0002f468 “” STAFF STAFF STAFF
7 0002f469 “” STAFF STAFF STAFF
8 0002f46c “” STAFF STAFF STAFF
9 0002f472 “” STAFF STAFF STAFF
10 0002f48a “” RN RN RN
# … with 85 more rows

[[63]] # A tibble: 31,421 × 29 numshift shiftampm D8 d9 H1N1 quarter sidi sidj i j 1 1 2 2009-07-09 18087 0 1 0002f35c 0002f… 1 2 2 1 2 2009-07-09 18087 0 1 0002f35c 0002f… 1 3 3 1 2 2009-07-09 18087 0 1 0002f35c 0002f… 1 4 4 1 2 2009-07-09 18087 0 1 0002f35c 0002f… 1 5 5 1 2 2009-07-09 18087 0 1 0002f35c 0002f… 1 6 6 1 2 2009-07-09 18087 0 1 0002f35c 0002f… 1 7 7 1 2 2009-07-09 18087 0 1 0002f35c 0002f… 1 8 8 1 2 2009-07-09 18087 0 1 0002f35c 0002f… 1 12 9 1 2 2009-07-09 18087 0 1 0002f35c 0002f… 1 14 10 1 2 2009-07-09 18087 0 1 0002f35c 0002f… 1 15 # … with 31,411 more rows, and 19 more variables: idi , idj , # i_participant_type , j_participant_type , staffi , # staffj , anycontact , combo , comboc , combo4 , # MD_CONTACTS , RN_CONTACTS , STAFF_CONTACTS , # PAT_CONTACTS , MD_WITHWHOM , RN_WITHWHOM , # STAFF_WITHWHOM , PAT_WITHWHOM , edgeweight

[[64]] # A tibble: 1,854 × 29 numshift shiftampm D8 d9 H1N1 quarter sidi sidj i j 1 10 1 2009-07-18 18096 0 1 0002f43c 00101… 1 12 2 10 1 2009-07-18 18096 0 1 0002f43c 00101… 1 14 3 10 1 2009-07-18 18096 0 1 0002f43c 00102… 1 21 4 10 1 2009-07-18 18096 0 1 0002f43c 00102… 1 23 5 10 1 2009-07-18 18096 0 1 0002f43c 00218… 1 30 6 10 1 2009-07-18 18096 0 1 0002f43c 00218… 1 32 7 10 1 2009-07-18 18096 0 1 0002f43c 00218… 1 35 8 10 1 2009-07-18 18096 0 1 0002f43c 00218… 1 39 9 10 1 2009-07-18 18096 0 1 0002f43c 00218… 1 40 10 10 1 2009-07-18 18096 0 1 0002f43c 00218… 1 45 # … with 1,844 more rows, and 19 more variables: idi , idj , # i_participant_type , j_participant_type , staffi , # staffj , anycontact , combo , comboc , combo4 , # MD_CONTACTS , RN_CONTACTS , STAFF_CONTACTS , # PAT_CONTACTS , MD_WITHWHOM , RN_WITHWHOM , # STAFF_WITHWHOM , PAT_WITHWHOM , edgeweight

[[65]] # A tibble: 3,637 × 203 sid d8 day year startd8time endd8time
1 0002d045 2009-07-09 NA NA 2009-07-09 20:00:00 NA
2 0002d078 2009-07-09 NA NA 2009-07-09 20:00:00 NA
3 0002d098 2009-07-09 NA NA 2009-07-09 20:00:00 NA
4 0002d10b 2009-07-09 NA NA 2009-07-09 20:00:00 NA
5 0002d151 2009-07-09 NA NA 2009-07-09 20:00:00 NA
6 0002d7c3 2009-07-09 NA NA 2009-07-09 20:00:00 NA
7 0002e016 2009-07-09 NA NA 2009-07-09 20:00:00 NA
8 0002e0d1 2009-07-09 NA NA 2009-07-09 20:00:00 NA
9 0002e120 2009-07-09 NA NA 2009-07-09 20:00:00 NA
10 0002e34f 2009-07-09 NA NA 2009-07-09 20:00:00 NA
# … with 3,627 more rows, and 197 more variables: shift_num_ampm , # mon , starttime , endtime , sdatetime , # edatetime , duration_observed , censusmax_perperson , # censusmedian_perperson , censusmean_perperson , shift_num , # ID , i , ShiftStart

[[66]] # A tibble: 222 × 203 sid d8 day year startd8time endd8time

1 0002f43c 2009-07-18 18 2009 2009-07-18 07:00:00 2009-07-18 15:00:00 2 0002f445 2009-07-18 18 2009 2009-07-18 07:00:00 2009-07-18 15:00:00 3 0002f469 2009-07-18 18 2009 2009-07-18 07:00:00 2009-07-18 15:00:00 4 0002f46c 2009-07-18 18 2009 2009-07-18 07:00:00 2009-07-18 15:00:00 5 0002f472 2009-07-18 18 2009 2009-07-18 07:00:00 2009-07-18 15:00:00 6 0002f49c 2009-07-18 18 2009 2009-07-18 07:00:00 2009-07-18 15:00:00 7 0002f4a3 2009-07-18 18 2009 2009-07-18 07:00:00 2009-07-18 15:00:00 8 0002f4e8 2009-07-18 18 2009 2009-07-18 07:00:00 2009-07-18 15:00:00 9 0002f4f9 2009-07-18 18 2009 2009-07-18 07:00:00 2009-07-18 15:00:00 10 0002f507 2009-07-18 18 2009 2009-07-18 07:00:00 2009-07-18 15:00:00 # … with 212 more rows, and 197 more variables: shift_num_ampm , # mon , starttime , endtime , sdatetime , # edatetime , duration_observed , censusmax_perperson , # censusmedian_perperson , censusmean_perperson , shift_num , # ID , i , ShiftStart

[[67]] # A tibble: 2,374 × 32 sid numshift d8 shift_ampm Reason_shortShift startd8time

1 0002f63d 1 2009-07-09 pm “” 2009-07-09 20:00:00 2 0003031b 1 2009-07-10 pm “” 2009-07-10 00:00:00 3 0002f652 1 2009-07-10 pm “” 2009-07-10 00:00:00 4 0003025d 1 2009-07-09 pm “” 2009-07-09 20:00:00 5 0002f656 1 2009-07-09 pm “” 2009-07-09 20:00:00 6 0002f620 1 2009-07-09 pm “” 2009-07-09 20:00:00 7 0002d7c3 1 2009-07-09 pm “” 2009-07-09 20:00:00 8 0002ea1a 1 2009-07-10 pm “” 2009-07-10 00:00:00 9 0003030f 1 2009-07-09 pm “” 2009-07-09 20:00:00 10 0002f488 1 2009-07-09 pm “” 2009-07-09 20:00:00 # … with 2,364 more rows, and 26 more variables: endd8time , # shift_d8_ampm , quarter , weekday , H1N1 , # ED_ARRIVAL , ed_departure , durationInED , # duration_UntilTag , Sex , Patient_Age_at_Visit , Race , # Chief_Complaint , Acuity , Arr_Mode , ED_Disposition , # daysinED , MinutesInED , AGE , ILI_Syndrome , # ILIwMissing , hrsinED , timeCoveredbyShift , …

[[68]] # A tibble: 153 × 31 sid numshift shift_ampm Reason_shortShift startd8time

1 0021825b 10 am Ended early because of illn… 2009-07-18 07:00:00 2 00218235 10 am Ended early because of illn… 2009-07-18 07:00:00 3 0021822a 10 am Ended early because of illn… 2009-07-18 07:00:00 4 00218344 10 am Ended early because of illn… 2009-07-18 07:00:00 5 0021822d 10 am Ended early because of illn… 2009-07-18 07:00:00 6 0021825e 10 am Ended early because of illn… 2009-07-18 07:00:00 7 0021836e 10 am Ended early because of illn… 2009-07-18 07:00:00 8 0021833b 10 am Ended early because of illn… 2009-07-18 07:00:00 9 0021822f 10 am Ended early because of illn… 2009-07-18 07:00:00 10 00218249 10 am Ended early because of illn… 2009-07-18 07:00:00 # … with 143 more rows, and 26 more variables: endd8time , # shift_d8_ampm , quarter , weekday , H1N1 , # ED_ARRIVAL , ed_departure , durationInED , # duration_UntilTag , Sex , Patient_Age_at_Visit , Race , # Chief_Complaint , Acuity , Arr_Mode , ED_Disposition , # daysinED , MinutesInED , AGE , ILI_Syndrome , # ILIwMissing , hrsinED , timeCoveredbyShift , …

[[69]] # A tibble: 6,944 × 6 SHIFT_NUM sid wdegmin_PS_STAFF WDEGMIN_SS wdegmin_PP wdegmin_PS_pat 1 1 0002d045 NA NA 33.8 14.6 2 1 0002d078 NA NA 23.0 1.47 3 1 0002d098 NA NA 99.6 60.2 4 1 0002d10b NA NA 43.1 5.6 5 1 0002d151 NA NA 210. 1.03 6 1 0002d7c3 NA NA 54.5 28.3 7 1 0002e016 NA NA 104. 199.
8 1 0002e0d1 NA NA 157. 44.1 9 1 0002e120 NA NA 16.2 3.62 10 1 0002e34f NA NA 214. 80.7 # … with 6,934 more rows

2 Data Import & Cleaning

2.1 Patients

2.1.1 Patient Location Dataset: pt_complete

The SAS data file, “completepat.sas7bdat,” contains RFID badge location (by room number) for all patients each second of every shift.

# 1a. read 'completepat.sas7bdat',
pt_complete <- read_sas(paste(data_path, "completepat.sas7bdat",
    sep = "/"))

Large data.frame, using the first six observations to code for data cleaning. The table is extremely wide (>4300 columns), I used pivot_longer() to reshape it by collapsing all location-by-second columns into two columns, names to seconds and values to location. This process causes there to be many repeated SIDs.

# 2a. subset first 10 observations for data
# transformation code preparation
pt_head <- head(pt_complete) %>%
    # 3a. Pivot the data.frame from wide to long by
    # placing all column names that start with 'floc'
    # into a new column, 'seconds,' and placing
    # respective observations for each 'floc' variable
    # into a 'location_num' column
pivot_longer(cols = starts_with("floc"), names_to = "seconds",
    values_to = "location_num") %>%
    # 4a. Remove the prefix 'floc' from `time_seconds`
    # and keep the digits as `seconds`
mutate(seconds = as.integer(str_replace(seconds, "floc",
    "")), shift_num_ampm = str_trim(shift_num_ampm), shift_num = as.integer(str_extract(shift_num_ampm,
    "[:digit:]+")), am_pm = str_extract(shift_num_ampm,
    "am|pm"), date = make_date(year = year, month = mon,
    day = day))  # %>% 
# 5. Filter out all rows for which no location was
# recorded filter(!is.na(location)) %>%

# View data frame structure glimpse(pt_head)
kable(head(pt_head))
sid shift_num_ampm d8 day mon year firstday seconds location_num shift_num am_pm date
0002d045 1pm 18087 9 7 2009 18087 1 NA 1 pm 2009-07-09
0002d045 1pm 18087 9 7 2009 18087 2 NA 1 pm 2009-07-09
0002d045 1pm 18087 9 7 2009 18087 3 NA 1 pm 2009-07-09
0002d045 1pm 18087 9 7 2009 18087 4 NA 1 pm 2009-07-09
0002d045 1pm 18087 9 7 2009 18087 5 NA 1 pm 2009-07-09
0002d045 1pm 18087 9 7 2009 18087 6 NA 1 pm 2009-07-09

2.1.2 Staff location: staff_complete

The SAS data file, “completestaff.sas7bdat,” contains RFID badge location (by room number) for all staff each second of every shift.

# ---- `staff_complete 1b. read
# 'completestaff.sas7bdat'
staff_complete <- read_sas(paste(data_path, "completestaff.sas7bdat",
    sep = "/"))
# 2a. subset first 10 observations for data
# transformation code preparation
staff_head <- head(staff_complete) %>%
    # 3a. Pivot the data.frame from wide to long by
    # placing all column names that start with 'floc'
    # into a new column, 'seconds,' and placing
    # respective observations for each 'floc' variable
    # into a 'location_num' column
pivot_longer(cols = starts_with("floc"), names_to = "seconds",
    values_to = "location_num") %>%
    # 4a. Remove the prefix 'floc' from `time_seconds`
    # and keep the digits as `seconds`
mutate(seconds = as.integer(str_replace(seconds, "floc",
    "")), shift_num_ampm = str_trim(shift_num_ampm), shift_num = as.integer(str_extract(shift_num_ampm,
    "[:digit:]+")), am_pm = str_extract(shift_num_ampm,
    "am|pm"), date = make_date(year = year, month = mon,
    day = day))  # %>% 
# 5. Filter out all rows for which no location was
# recorded filter(!is.na(location)) %>% View data
# frame structure str(staff_head) glimpse(staff_head)
kable(head(staff_head))
sid d8 day year shift_num_ampm mon firstday seconds location_num shift_num am_pm date
0002f4e2 2009-07-09 9 2009 1pm 7 18087 1 NA 1 pm 2009-07-09
0002f4e2 2009-07-09 9 2009 1pm 7 18087 2 NA 1 pm 2009-07-09
0002f4e2 2009-07-09 9 2009 1pm 7 18087 3 NA 1 pm 2009-07-09
0002f4e2 2009-07-09 9 2009 1pm 7 18087 4 NA 1 pm 2009-07-09
0002f4e2 2009-07-09 9 2009 1pm 7 18087 5 NA 1 pm 2009-07-09
0002f4e2 2009-07-09 9 2009 1pm 7 18087 6 NA 1 pm 2009-07-09

2.1.3 List of RFID badge encounters (i.e., clinical interactions) as vertices: edge_list

Read & print data from “allshifts_edges.sas7bdat” and “edges2.sas7bdat.”

# ---- 'edge_list` ---- 1c. read
# 'allshifts_edges.sas7bdat'
edge_list <- read_sas(paste(data_path, "allshifts_edges.sas7bdat",
    sep = "/"))
edge_list2 <- read_sas(paste(data_path, "edges2.sas7bdat",
    sep = "/"))
# Print the first 6 observations of edge_list
kable(head(edge_list))
i any staffi idi d8 H1N1 quarter shiftampm d9 edgeweight j staffj combo idj comboc
1 1 1 7920091 2009-07-09 0 1 2 18087 0.5247222 2 1 2 7920092 2 staff-staff
1 1 1 7920091 2009-07-09 0 1 2 18087 3.7672222 3 1 2 7920093 2 staff-staff
1 1 1 7920091 2009-07-09 0 1 2 18087 1.1116667 4 1 2 7920094 2 staff-staff
1 1 1 7920091 2009-07-09 0 1 2 18087 0.4872222 5 1 2 7920095 2 staff-staff
1 1 1 7920091 2009-07-09 0 1 2 18087 0.7936111 6 1 2 7920096 2 staff-staff
1 1 1 7920091 2009-07-09 0 1 2 18087 0.5130556 7 1 2 7920097 2 staff-staff 
# Print out the variable labels for all columns of
# edge_list (object varbles1)
varbles1 <- var_label(edge_list)
paste(names(varbles1), varbles1, sep = ": ")

[1] “i: one member of contact pair (find real id using id_sid_matchuplist)”
[2] “any: any contact 1yes 0no”
[3] “staffi: i is a staff member 1yes 0no”
[4] “idi: id for i made of d8 and i”
[5] “d8: 1st d8 in the shift”
[6] “H1N1: in H1N1 season 1yes”
[7] “quarter: study qtr, July-Sept09 is first qtr”
[8] “shiftampm: time of shift (1day, 2night)”
[9] “d9: day of week that shift started”
[10] “edgeweight: hours of contact”
[11] “j: second member of contact pair (find real id using id_sid_matchuplist)” [12] “staffj: j is a staff member 1 yes 0no”
[13] “combo: type of contact 0(pp) 1(ps) 2(ss)”
[14] “idj: id for j made of d8 and j”
[15] “comboc: patient-staff combinations”

# Print the first 6 observations of edge_list2
kable(head(edge_list2))
numshift shiftampm D8 d9 H1N1 quarter sidi sidj i j idi idj i_participant_type j_participant_type staffi staffj anycontact combo comboc combo4 MD_CONTACTS RN_CONTACTS STAFF_CONTACTS PAT_CONTACTS MD_WITHWHOM RN_WITHWHOM STAFF_WITHWHOM PAT_WITHWHOM edgeweight
1 2 2009-07-09 18087 0 1 0002f35c 0002f445 1 2 7920091 7920092 STAFF STAFF 1 1 1 2 2 staff-staff STAFF-STAFF 0 0 1 0 STAFF 0.5247222
1 2 2009-07-09 18087 0 1 0002f35c 0002f468 1 3 7920091 7920093 STAFF STAFF 1 1 1 2 2 staff-staff STAFF-STAFF 0 0 1 0 STAFF 3.7672222
1 2 2009-07-09 18087 0 1 0002f35c 0002f469 1 4 7920091 7920094 STAFF STAFF 1 1 1 2 2 staff-staff STAFF-STAFF 0 0 1 0 STAFF 1.1116667
1 2 2009-07-09 18087 0 1 0002f35c 0002f46c 1 5 7920091 7920095 STAFF STAFF 1 1 1 2 2 staff-staff STAFF-STAFF 0 0 1 0 STAFF 0.4872222
1 2 2009-07-09 18087 0 1 0002f35c 0002f472 1 6 7920091 7920096 STAFF STAFF 1 1 1 2 2 staff-staff STAFF-STAFF 0 0 1 0 STAFF 0.7936111
1 2 2009-07-09 18087 0 1 0002f35c 0002f495 1 7 7920091 7920097 STAFF STAFF 1 1 1 2 2 staff-staff STAFF-STAFF 0 0 1 0 STAFF 0.5130556 
# Print out the variable labels for all columns of
# edge_list2 (object varbles2)
varbles2 <- var_label(edge_list2)
paste(names(varbles2), varbles2, sep = ": ")

[1] “numshift: shift number”
[2] “shiftampm: time of shift (1day, 2night)”
[3] “D8: first date in shift”
[4] “d9: day of week that shift started”
[5] “H1N1: in H1N1 season 1yes”
[6] “quarter: study qtr, July-Sept09 is first qtr”
[7] “sidi: SID OF NODE I”
[8] “sidj: SID OF NODE J”
[9] “i: one member of contact pair (find real id using id_sid_matchuplist)” [10] “j: arbitrary sid for this d8”
[11] “idi: id for i made of d8 and i”
[12] “idj: id for j made of d8 and j”
[13] “i_participant_type: participant type”
[14] “j_participant_type: participant type”
[15] “staffi: i is a staff member 1yes 0no”
[16] “staffj: j is a staff member 1 yes 0no”
[17] “anycontact: any contact 1yes 0no”
[18] “combo: type of contact 0(pp) 1(ps) 2(ss)”
[19] “comboc: patient-staff combinations”
[20] “combo4: DETAILED CONTACT DESCRIPTION (PARTICIPANT TYPE COMBINATIONS)” [21] “MD_CONTACTS: the edge has at least one MD node”
[22] “RN_CONTACTS: the edge has at least one RN node”
[23] “STAFF_CONTACTS: the edge has at least one STAFF node”
[24] “PAT_CONTACTS: the edge has at least one PATIENT node”
[25] “MD_WITHWHOM: TYPE OF CONTACT PARTNER (MD)”
[26] “RN_WITHWHOM: TYPE OF CONTACT PARTNER (RN)”
[27] “STAFF_WITHWHOM: TYPE OF CONTACT PARTNER (STAFF)”
[28] “PAT_WITHWHOM: TYPE OF CONTACT PARTNER (PAT)”
[29] “edgeweight: hours of contact”

2.2 ED Geography:

List of files in “Data/Data_Reference/Geography”:

# Create a list of all items in the current working
# directory
list.files(paste(data_path, "/Data_Reference/Geography"))

character(0)

# Print directory file list

2.2.0.1 Room categories/locations: room_categories

room_categories <- read_xlsx(paste(data_path, "room_locations",
    "room categories.xlsx", sep = "/"))
room_categories2 <- read_xlsx(paste(data_path, "room_locations",
    "room categories2.xlsx", sep = "/"))
room_categories20131204 <- read_xlsx(paste(data_path, "room_locations",
    "room categories2 20131204.xlsx", sep = "/"))
room_categories20131204_area <- read_xlsx(paste(data_path,
    "room_locations", "room categories2 20131204 area.xlsx",
    sep = "/"))
room_location <- read_xlsx(paste(data_path, "room_locations",
    "Room Locations and Square Footage - with Correction.xlsx",
    sep = "/"))
geographic_lst <- list(room_categories, room_categories2,
    room_categories20131204, room_categories20131204_area,
    room_location)
# Print the first 6 rows form each room
# category/location file iteratively with
# `purrr::map()`
map(geographic_lst, c(head, kable))
[[1]]
Location ID Location Name Space Type Location Area (Sq Ft) cat …6 …7
68 IMAGING AND CONF RM Administrative Support 314 1 NA NA
71 ED CONF. ROOM Administrative Support 381 1 NA NA
75 OFFICE AREA Administrative Support 938 1 NA NA
77 OFFICE AREA Administrative Support 708 1 NA NA
25 CLEAN UTILITY Clinical Support 143 2 NA NA
26 SOILED UTILITY Clinical Support 157 2 NA NA
[[2]]
correct locindex is location id minus 1 Location ID …3 Location Name Space Type Location Area (Sq Ft) cat …8 …9
NA 68 NA IMAGING AND CONF RM Administrative Support 314 1 NA NA
NA 71 NA ED CONF. ROOM Administrative Support 381 1 NA NA
NA 75 NA OFFICE AREA Administrative Support 938 1 NA NA
NA 77 NA OFFICE AREA Administrative Support 708 1 NA NA
NA 25 NA CLEAN UTILITY Clinical Support 143 2 NA NA
NA 26 NA SOILED UTILITY Clinical Support 157 2 NA NA
[[3]]
correct locindex is location id minus 1 incorrect Location ID Location Name Space Type Location Area (Sq Ft) cat …7 …8
NA 2 ED RADIOLOGY Diagnotics 327 8 NA NA
NA 3 ED ROOM 1 Patient Care - Acute Care 145 5 walls/doors NA
NA 4 ED ROOM 2 Patient Care - Acute Care 172 5 NA NA
NA 5 ED ROOM 3 Patient Care - Acute Care 172 5 NA NA
NA 6 ED ROOM 4 Patient Care - Acute Care 157.5 5 NA NA
NA 7 ED ROOM 5 Patient Care - Acute Care 157.5 5 NA NA
[[4]]
correct locindex is location id minus 1 incorrect Location ID Location Name Space Type Location Area (Sq Ft) cat
1 2 ED RADIOLOGY Diagnotics 327.0 8
2 3 ED ROOM 1 Patient Care - Acute Care 145.0 5
3 4 ED ROOM 2 Patient Care - Acute Care 172.0 5
4 5 ED ROOM 3 Patient Care - Acute Care 172.0 5
5 6 ED ROOM 4 Patient Care - Acute Care 157.5 5
6 7 ED ROOM 5 Patient Care - Acute Care 157.5 5
[[5]]
Location ID Location Name Space Type Location Area (Sq Ft) …5
2 ED RADIOLOGY Diagnotics 327 NA
3 ED ROOM 1 Patient Care - Acute Care 145 NA
4 ED ROOM 2 Patient Care - Acute Care 172 NA
5 ED ROOM 3 Patient Care - Acute Care 172 NA
6 ED ROOM 4 Patient Care - Acute Care 157.5 NA
7 ED ROOM 5 Patient Care - Acute Care 157.5 NA

2.3 Patient attributes

2.3.1 Patient ID to RFID badge SID key: id_sid

Read & print data from “id_sid_matchup.sas7bdat” & “id_sid_matchup.sas7bdat.”

# 1d. read 'id_sid_matchup.sas7bdat' into id_sid and
# 'id_sid_matchup2.sas7bdat' into id_sid2
id_sid <- read_sas(paste(data_path, "id_sid_matchup.sas7bdat",
    sep = "/"))
id_sid2 <- read_sas(paste(data_path, "id_sid_matchup2.sas7bdat",
    sep = "/"))
# Print the first 6 rows of id_sid
kable(head(id_sid))
sid day mon staff newsid
0002f35c 9 7 1 1
0002f445 9 7 1 2
0002f468 9 7 1 3
0002f469 9 7 1 4
0002f46c 9 7 1 5
0002f472 9 7 1 6 
# Print the first 6 rows of id_sid2
kable(head(id_sid2))
sid day mon staff newsid year d8 ShiftStart ShiftEnd shift_ampm Reason_shortShift startd8time shift_d8_ampm shift_num_ampm quarter weekday H1N1 SevenToTwelve numshift
0002f35c 9 7 1 1 2009 2009-07-09 20:00:00 23:59:59 pm 2009-07-09 20:00:00 200979pm 1pm 1 1 0 1 1
0002f445 9 7 1 2 2009 2009-07-09 20:00:00 23:59:59 pm 2009-07-09 20:00:00 200979pm 1pm 1 1 0 1 1
0002f468 9 7 1 3 2009 2009-07-09 20:00:00 23:59:59 pm 2009-07-09 20:00:00 200979pm 1pm 1 1 0 1 1
0002f469 9 7 1 4 2009 2009-07-09 20:00:00 23:59:59 pm 2009-07-09 20:00:00 200979pm 1pm 1 1 0 1 1
0002f46c 9 7 1 5 2009 2009-07-09 20:00:00 23:59:59 pm 2009-07-09 20:00:00 200979pm 1pm 1 1 0 1 1
0002f472 9 7 1 6 2009 2009-07-09 20:00:00 23:59:59 pm 2009-07-09 20:00:00 200979pm 1pm 1 1 0 1 1 
# Print variable labels
idsid_varbles <- var_label(id_sid)
idsid2_varbles <- var_label(id_sid2)

idsid_labeltable <- paste(names(idsid_varbles), idsid_varbles,
    sep = ": ")
kable(tibble(idsid_labeltable))
idsid_labeltable
sid: NULL
day: NULL
mon: NULL
staff: NULL
newsid: NULL 
idsid2_labeltable <- paste(names(idsid2_varbles), idsid2_varbles,
    sep = ": ")
kable(tibble(idsid2_labeltable))
idsid2_labeltable
sid: NULL
day: NULL
mon: NULL
staff: NULL
newsid: NULL
year: NULL
d8: first date in shift
ShiftStart: ShiftStart
ShiftEnd: ShiftEnd
shift_ampm: shift_ampm
Reason_shortShift: Reason_shortShift
startd8time: NULL
shift_d8_ampm: NULL
shift_num_ampm: NULL
quarter: 1summer 2fall **correct definition of weekend for this study is from 7am Saturday to 6:59am Monday
weekday: NULL
H1N1: NULL
SevenToTwelve: NULL
numshift: NULL

2.3.2 Patient Acuity: pt_acuity

Read & print patient acuity data in “ACUITY-patients.xlsx,” which is an Excel workbook with 3 sheets.

# 1e. read 'ACUITY-patients.xlsx
pt_acuity <- read_xlsx(paste(data_path, "ACUITY-patients.xlsx",
    sep = "/"))
# str(pt_acuity)
pt_acuity_s2 <- read_xlsx(paste(data_path, "ACUITY-patients.xlsx",
    sep = "/"), sheet = 2, range = "A2:H83")
# str(pt_acuity_s2)
pt_acuity_s3 <- read_xlsx(paste(data_path, "ACUITY-patients.xlsx",
    sep = "/"), sheet = 3, range = "A2:G83")
# str(pt_acuity_s3)

The first sheet lists the number of patients in each ESI acuity level (columns) by shift (rows). The other two sheets appear to be variations of the first.

# Patient acuity (Emergency Severity Index; ESI)
# counts by shift
kable(head(pt_acuity))
Shift Acuity
1 3 Urgent
1 3 Urgent
1 4 Stable
1 4 Stable
1 2 Emergent
1 3 Urgent 
# Pivot_wider to view number of patients in each ESI
# category by shift
pt_acuity %>%
    group_by(Acuity) %>%
    count(Shift) %>%
    pivot_wider(names_from = Acuity, values_from = n) %>%
    head() %>%
    kable()
Shift 1 Immediate 2 Emergent 3 Urgent 4 Stable 5 Non Urgent Not Recorded
1 1 18 45 5 2 3
38 1 23 48 15 2 2
56 1 27 41 18 NA 2
63 6 19 49 15 NA 4
98 1 22 37 8 NA NA
102 1 17 34 12 NA NA 
# Print the first 6 rows of the other two sheets in
# the xlsx file
kable(head(pt_acuity_s2))
Shift 1 Immediate 2 Emergent 3 Urgent 4 Stable 5 Non Urgent Not Recorded Grand Total
1 1 18 45 5 2 3 74
8 0 30 34 12 1 5 82
10 0 19 30 13 2 0 64
17 0 24 50 19 1 1 95
19 0 22 48 17 0 2 89
23 0 14 43 13 1 1 72 
kable(head(pt_acuity_s3))
Shift 1 Immediate 2 Emergent 3 Urgent 4 Stable 5 Non Urgent Not Recorded
1 0.0135135 0.2432432 0.6081081 0.0675676 0.0270270 0.0405405
8 0.0000000 0.3658537 0.4146341 0.1463415 0.0121951 0.0609756
10 0.0000000 0.2968750 0.4687500 0.2031250 0.0312500 0.0000000
17 0.0000000 0.2526316 0.5263158 0.2000000 0.0105263 0.0105263
19 0.0000000 0.2471910 0.5393258 0.1910112 0.0000000 0.0224719
23 0.0000000 0.1944444 0.5972222 0.1805556 0.0138889 0.0138889

3 SessionInfo

sessioninfo::session_info() %>%
    details::details(summary = "Current session info", open = FALSE)
Current session info 

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.1.2 (2021-11-01)
 os       macOS Big Sur 10.16
 system   x86_64, darwin17.0
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       America/New_York
 date     2022-03-13
 pandoc   2.17.1.1 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package     * version    date (UTC) lib source
 assertthat    0.2.1      2019-03-21 [1] CRAN (R 4.1.0)
 backports     1.4.1      2021-12-13 [1] CRAN (R 4.1.0)
 broom         0.7.12     2022-01-28 [1] CRAN (R 4.1.2)
 bslib         0.3.1      2021-10-06 [1] CRAN (R 4.1.0)
 cellranger    1.1.0      2016-07-27 [1] CRAN (R 4.1.0)
 cli           3.2.0      2022-02-14 [1] CRAN (R 4.1.2)
 clipr         0.8.0      2022-02-22 [1] CRAN (R 4.1.2)
 colorspace    2.0-3      2022-02-21 [1] CRAN (R 4.1.2)
 crayon        1.5.0      2022-02-14 [1] CRAN (R 4.1.2)
 DBI           1.1.2      2021-12-20 [1] CRAN (R 4.1.0)
 dbplyr        2.1.1      2021-04-06 [1] CRAN (R 4.1.0)
 desc          1.4.0      2021-09-28 [1] CRAN (R 4.1.0)
 details     * 0.2.1      2020-01-12 [1] CRAN (R 4.1.0)
 digest        0.6.29     2021-12-01 [1] CRAN (R 4.1.0)
 dplyr       * 1.0.8      2022-02-08 [1] CRAN (R 4.1.2)
 ellipsis      0.3.2      2021-04-29 [1] CRAN (R 4.1.0)
 evaluate      0.15       2022-02-18 [1] CRAN (R 4.1.2)
 fansi         1.0.2      2022-01-14 [1] CRAN (R 4.1.2)
 fastmap       1.1.0      2021-01-25 [1] CRAN (R 4.1.0)
 forcats     * 0.5.1      2021-01-27 [1] CRAN (R 4.1.0)
 formatR       1.11       2021-06-01 [1] CRAN (R 4.1.0)
 fs            1.5.2      2021-12-08 [1] CRAN (R 4.1.0)
 generics      0.1.2      2022-01-31 [1] CRAN (R 4.1.2)
 ggplot2     * 3.3.5      2021-06-25 [1] CRAN (R 4.1.0)
 glue          1.6.2      2022-02-24 [1] CRAN (R 4.1.2)
 gt          * 0.4.0      2022-02-15 [1] CRAN (R 4.1.2)
 gtable        0.3.0      2019-03-25 [1] CRAN (R 4.1.0)
 haven       * 2.4.3      2021-08-04 [1] CRAN (R 4.1.0)
 highr         0.9        2021-04-16 [1] CRAN (R 4.1.0)
 hms           1.1.1      2021-09-26 [1] CRAN (R 4.1.0)
 htmltools     0.5.2      2021-08-25 [1] CRAN (R 4.1.0)
 httr          1.4.2      2020-07-20 [1] CRAN (R 4.1.0)
 igraph      * 1.2.11     2022-01-04 [1] CRAN (R 4.1.2)
 jquerylib     0.1.4      2021-04-26 [1] CRAN (R 4.1.0)
 jsonlite      1.8.0      2022-02-22 [1] CRAN (R 4.1.2)
 kableExtra  * 1.3.4      2021-02-20 [1] CRAN (R 4.1.0)
 knitr       * 1.37       2021-12-16 [1] CRAN (R 4.1.0)
 labelled    * 2.9.0      2021-10-29 [1] CRAN (R 4.1.0)
 lifecycle     1.0.1      2021-09-24 [1] CRAN (R 4.1.0)
 lubridate   * 1.8.0      2021-10-07 [1] CRAN (R 4.1.0)
 magrittr      2.0.2      2022-01-26 [1] CRAN (R 4.1.2)
 modelr        0.1.8      2020-05-19 [1] CRAN (R 4.1.0)
 munsell       0.5.0      2018-06-12 [1] CRAN (R 4.1.0)
 pillar        1.7.0      2022-02-01 [1] CRAN (R 4.1.2)
 pkgconfig     2.0.3      2019-09-22 [1] CRAN (R 4.1.0)
 png           0.1-7      2013-12-03 [1] CRAN (R 4.1.0)
 prettydoc     0.4.1      2021-01-10 [1] CRAN (R 4.1.0)
 purrr       * 0.3.4      2020-04-17 [1] CRAN (R 4.1.0)
 R6            2.5.1      2021-08-19 [1] CRAN (R 4.1.0)
 Rcpp          1.0.8      2022-01-13 [1] CRAN (R 4.1.2)
 readr       * 2.1.2      2022-01-30 [1] CRAN (R 4.1.2)
 readxl      * 1.3.1      2019-03-13 [1] CRAN (R 4.1.0)
 rematch       1.0.1      2016-04-21 [1] CRAN (R 4.1.0)
 reprex        2.0.1      2021-08-05 [1] CRAN (R 4.1.0)
 rlang         1.0.1      2022-02-03 [1] CRAN (R 4.1.2)
 rmarkdown     2.11       2021-09-14 [1] CRAN (R 4.1.0)
 rprojroot     2.0.2      2020-11-15 [1] CRAN (R 4.1.0)
 rstudioapi    0.13       2020-11-12 [1] CRAN (R 4.1.0)
 rvest         1.0.2      2021-10-16 [1] CRAN (R 4.1.0)
 sass          0.4.0      2021-05-12 [1] CRAN (R 4.1.0)
 scales        1.1.1      2020-05-11 [1] CRAN (R 4.1.0)
 sessioninfo   1.2.2      2021-12-06 [1] CRAN (R 4.1.0)
 stringi       1.7.6      2021-11-29 [1] CRAN (R 4.1.0)
 stringr     * 1.4.0      2019-02-10 [1] CRAN (R 4.1.0)
 svglite       2.1.0      2022-02-03 [1] CRAN (R 4.1.2)
 systemfonts   1.0.4      2022-02-11 [1] CRAN (R 4.1.2)
 tibble      * 3.1.6      2021-11-07 [1] CRAN (R 4.1.0)
 tidyr       * 1.2.0      2022-02-01 [1] CRAN (R 4.1.2)
 tidyselect    1.1.1.9000 2022-03-02 [1] Github (r-lib/tidyselect@00e8f39)
 tidyverse   * 1.3.1      2021-04-15 [1] CRAN (R 4.1.0)
 tzdb          0.2.0      2021-10-27 [1] CRAN (R 4.1.0)
 utf8          1.2.2      2021-07-24 [1] CRAN (R 4.1.0)
 vctrs         0.3.8.9001 2022-03-02 [1] Github (r-lib/vctrs@f0c4ead)
 viridisLite   0.4.0      2021-04-13 [1] CRAN (R 4.1.0)
 webshot       0.5.2      2019-11-22 [1] CRAN (R 4.1.0)
 withr         2.4.3      2021-11-30 [1] CRAN (R 4.1.0)
 xfun          0.29       2021-12-14 [1] CRAN (R 4.1.0)
 xml2          1.3.3      2021-11-30 [1] CRAN (R 4.1.0)
 yaml          2.3.5      2022-02-21 [1] CRAN (R 4.1.2)

 [1] /Library/Frameworks/R.framework/Versions/4.1/Resources/library

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